Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data

1 Introduction

The current machine learning paradigm has achieved great success in many scenarios, such as medicine, finance, and ecology. However, achieving a well-performing model requires several essential conditions, such as access to abundant high-quality labeled data, strong computational resources, and expertise in feature engineering and algorithms. Consequently, ordinary individuals face a significant burden when aiming to build a high-quality model. Moreover, the challenges of data privacy and catastrophic forgetting arise when reusing or adapting a trained model among different users. In order to address these challenges simultaneously, the learnware paradigm [Zhou, 2016; Zhou and Tan, 2022] aims to establish a model market that assists users in solving their tasks by leveraging existing efforts instead of starting from scratch. The design of learnware market is to allow well-performed models submitted by developers can be used "beyond-whatwas-submitted" instead of "as-what-was-submitted"; in other words, the models can be reused to help tasks that were not planned/targeted by their developers.

A learnware is comprised of a well-performed trained model and a specification that describes its capabilities, allowing the model to be adequately identified according to the requirements of future tasks [Zhou and Tan, 2022]. The learnware market accommodates various learnwares. Machine learning developers can submit their well-performing models spontaneously to the market, and the market assigns specifications to accepted models. Once the learnware market is established, the user can exploit it by specifying her task requirement to the market and reusing recommended learnwares from the market. It is important to note that the learnware market have no access to the original task data of developers and users. Therefore, the specification plays a central role when the market identifies useful models based on user requirements. Recently the Reduced Kernel Mean Embedding (RKME) specification [Zhou and Tan, 2022] was proposed, describing the model as an element in the reproducing kernel Hilbert space (RKHS), which is also called the specification space. Based on the RKME specification, different learnware identification and reuse algorithms have been proposed [Wu et al., 2023; Zhang et al., 2021].

Current studies on the learnware paradigm require that all learnwares in a specification space share the same feature space. However, in real-world scenarios, models are usually from different feature spaces even if they solve the same machine learning task. For instance, in the extensively utilized clinical database of critical care units [Johnson et al., 2016], data is organized across 26 tables such as inputevents, outputevents, labevents etc. One crucial medical task is to predict the length of stay (LOS) for patients in the intensive care unit (ICU), aiming at improving scheduling and hospital resource management [Purushotham et al., 2018; Harutyunyan et al., 2019]. When solving this task, models are built on the centralized data aggregated from selected tables. Due to varying prior medical knowledge, different models developed for the same task may possess different feature spaces since training data are centralized from different subsets of tables. This situation is illustrated in Figure 1. For such a realistic scenario, an intriguing question arises: Can the market accommodate all these models with heterogeneous feature spaces and identify helpful models for new user tasks? In such a medical scenario, the user could be a newly established hospital lacking access to precious and sensitive raw data concerning critically ill patients.

In this problem, exploring the relationship between different feature spaces is an essential step. However, temporary solutions often require a significant amount of co-occurrence data across all feature spaces to reveal this relationship [Yang et al., 2015; Wang et al., 2016; Tan et al., 2022], which is challenging to obtain or even absent in real-world scenarios. Moreover, due to privacy concerns within the learnware paradigm, access to the raw data of submitted models is prohibited. Consequently, the market needs to establish the relationship between different feature spaces without relying on the raw data of models or additional auxiliary co-occurrence data. To tackle this challenge, our proposed solution is that the market constructs the relationship of heterogeneous feature spaces based on the specifications provided with the submitted models. Based on this idea, with an increasing number of models accommodated by the market, the established relationship can become more accurate.

In this paper, we realize a heterogeneous learnware market. The market accommodates heterogeneous models from developers without requiring additional auxiliary data while ensuring the privacy of the submitted models. When the user exploits the market, the market can recommend helpful models whose feature spaces even differs from the user task. In summary, the contributions are threefold.

• We present a general framework for addressing the heterogeneous learnware problem without requiring additional auxiliary data. RKME specifications are utilized to establish connections between different feature spaces of models and generate corresponding mapping functions. These mapping functions are employed to adjust the specifications of models during market construction, align task requirements, and address missing features when the user exploit the market and reuse helpful models.
• Our general framework can be integrated with various existing subspace learning methods, and we provide an implementation based on matrix factorization. Additionally, we present a two-stage procedure for constructing and utilizing

the heterogeneous learnware market.

• Experiments on both synthetic datasets and real-world tasks validate the efficacy of our methods.

The paper proceeds as follows. Section 2 provides the preliminaries, followed by Section 3 where the problem is formulated. Our general framework is introduced in Section 4, and a matrix factorization-based implementation is presented in Section 5. The experimental results are presented in Section 6. Section 7 discusses related topics, and finally, Section 8 summarizes the work.

2 Preliminary

In this section, we give a brief introduction to the pioneer work realizing the specification which can accommodate homogeneous learnwares via Reduced Kernel Mean Embedding (RKME) [Zhou and Tan, 2022] based on Kernel Mean Embedding (KME) [Smola et al., 2007].

Kernel Mean Embedding. KME gives a new presentation to the distribution which supports convenient operations like mean calculation. More specifically, KME maps a distribution P defined over X to an element in a reproducing kernel Hilbert space (RKHS) H as µk(P) := ∫X k(x, ·)dP(x), where k : X × X → R is a symmetric and positive definite kernel function [Sch¨olkopf and Smola, 2002] with associated RKHS H. When equipped with the characteristic kernel such as Gaussian kernel, no information about the distribution P will be lost [Sriperumbudur et al., 2011]. When provided with a data set {xi}ni=1 sampled from P, the empirical estimation of KME is ˆµk(P) := 1n ∑ni=1 k (xi, ·).

Reduced Kernel Mean Embedding. KME is a potential specification due to several favorable properties. However, the access to the raw data violates the privacy concern which the specification needs. Based on KME, RKME specification [Zhou and Tan, 2022] was recently proposed, with the main idea of using the reduced set containing minor weighted samples {(βj , zj)}mj=1 to approximate the empirical KME of the original data set {xi}ni=1 . The reduced set is generated by

with non-negative constraints of coefficients {βj}mj=1 . The RKME Φ(·) = ∑mj=1 βjk (zj, ·) ∈ H serves as the specification and the RKHS H is called specification space. This specification captures the major information of the distribution P and the raw data is not exposed which satisfies the data privacy property which the specification needs.

3 Formulation

We consider the entire feature space Xall as a composition of Q components: Xall = X1×· · ·×XQ. The feature space of the developer X up and the user X us are the Cartesian product of several components ×i∈CXi where C is the set of component indices. We assume the market encounters T kinds of feature space for submitted models X up 1 , · · · ,X up T .

The learnware paradigm consists of the submitting stage and the deploying stage. In the submitting stage, there are N developers submit their models. Each model fi is trained on the labeled data set Di := {(xij, yij)}ni j=1 defined over X up ϕi × Y where ϕi ∈ {1, · · · , T}. In addition to the welltrained model fi, the developer also provides the raw RKME specification sraw i to themarket. Themarket receives a total of N models along with their raw specifications {fi, sraw i }Ni=1 . Temporarily, raw specifications are located in T different specification spaces, and thus, the learnware recommendation can only be conducted in each isolated specification space, which restricts the scope of learnware recommendation. In order to recommend and reuse all heterogeneous learnwares, the market will combine all these specification spaces and generate a global specification space, and then, reassign the specification si for the model fi. The heterogeneous learnware market is built as {fi, si}Ni=1 .

In the deploying stage, the user has a unlabeled dataset D0 := {x0j}n0 j=1 defined over X us . The user generates the raw requirement sraw 0 and passes it to the market. The market recommends several helpful learnwares {fi|i ∈ I} based on specifications {si}Ni=1 and the requirement sraw 0 accordingly where I is the indices of recommended learnwares. The user solves her problem by reusing learnwares {fi|i ∈ I} to construct a model f0 and tries to minimizing its expected error E(x,y)∼D0 (l(f0(x), y)) where D0 is the data distribution of the user. The overall procedure is illustrated in Figure 2.

4 General Framework

In this section, we provide a general framework for handling the heterogeneous learnware problem based on the RKME specification. The problem involves two main challenges:

• How to align raw specifications of models {sraw i }Ni=1 and the raw user's requirement sraw 0 located in different spaces?
• How does the user reuse recommended models whose feature space may be different from the user's task?

The first problem is fundamental because the learnware recommendation relies on model specifications and user requirements being in the same space, and the recommendation procedure significantly impacts the second reuse problem. To address these challenges simultaneously, our general framework proposes a key idea: connecting different feature spaces associated with models and future user tasks to a shared subspace using only raw specifications of submitted models.

One approach is connecting T feature spaces of models {X up j }Tj=1 to a common subspace and generate 2T mapping functions, which consists of T projection functions mapping the data from original feature space to an identical subspace {hj : X up j ↦→ Xsub}Tj=1 and T reconstruction functions mapping the data in the subspace to the original feature space {gj : Xsub ↦→ X up j }Tj=1 . However, this approach has a significant limitation. When the user's task feature space X us , is not included in {X up j }Tj=1 , themarket is unable to recommend models or assist the user in reusing models. Noticing that the feature spaces of submitted models {X up j }Tj=1 and user's task X us are actually the Cartesian product of a subset of component feature spaces {Xk}Qk=1 , it is more effective to connect component feature spaces {Xk}Qk=1 to a subspace and generate 2Q(Q ≪ T) functions {hk : Xk ↦→ Xsub}Qk=1 , {gk : Xsub ↦→ Xk}Qk=1 instead. This approach heavily reduces the number of mapping functions required and can handle future user tasks defined on any combinations of component feature spaces. The generated mapping functions provide a unified solution to address the aforementioned challenges.

• The raw specifications {sraw i }Ni=1 and raw requirement sraw 0 are generated by RKME and they are based on the reduced set containing minor samples and their weights. By utilizing projection functions {hk : Xk ↦→ Xsub}Qk=1 , samples defined on different feature spaces {X up j }Tj=1 ,X us can be aligned to the common subspace Xsub.
• When the user wants to reuse a model with different feature space, i.e., X up j ̸= X us , missing features that model prediction needs must be filled up, the user can fist project the data to the subspace via projection functions {hk : Xk ↦→ Xsub}Qk=1 and then reconstruct them in corresponding missing component feature spaces via reconstruction functions {gk : Xsub ↦→Xk}Qk=1 .

In the following, we describe the general framework about how to learn a common subspace and generate mapping functions using raw specifications of models. The market receives N models from T feature spaces with their raw specifications implemented by RKME and the corresponding reduced sets are {sraw i := {(βij, zij)}mi j=1 }Ni=1 where mi is the size of the i-th reduced set. For simplicity, we define the union set of all reduced sets as U := {βl, zl}ml=1 where m = ∑Ni=1 mi.

The procedure of subspace learning uses whole reduced set U to optimize mapping functions between component feature spaces and subspace {hk, gk}Qk=1 , it also generate sample projections {vl}ml=1 . The objective accordingly is

The objective comprises two parts: the weighted subspace learning loss and the similarity loss. Specifically, the first term calculates the sum of the losses of each sample zl in the reduced set, weighted by βl, during subspace learning. Since each sample's feature space is a combination of multiple components Xk, the subspace learning loss can be decomposed as L(zl, vl) = ∑k∈C L(z(k) l , v(k) l ), where C represents the set of component indices, z(k) l and v(k) l are the slices of zl, vl on Xk. The loss L(z(k) l , v(k) l ) can be defined in three ways:

• Defined on the original component feature space Xk. For example, Lo = ∥z(k) l - gk(v(k) l )∥. After obtaining gk, hk can be optimized by minv ∑l∈Ik ∥z(k) l - gk(v)∥ where Ik is indices of samples whose feature space contains Xk.
• Defined on the identical subspace Xsub. For example, Ls = ∥hk(z(k) l )-v(k) l ∥. After obtaining hk, gk can be optimized by minz ∑l∈Ik ∥hk(z) - v(k) l ∥.
• Defined on both component feature space and subspace like Lo + Ls. {hk, gk}Qk=1 can be obtained simultaneously.

The second item indicates the similarity loss. This loss aims to ensure that when two samples xi and xj are similar, their respective projections vi and vj exhibit similarity as well. To be noticed that zi, zj may defined on different feature space. In such a case, the similarity can be calculated on the intersection of two feature spaces, which is still the Cartesian product of some components within {Xk}Qk=1 .

The paper presents a general framework that supports three types of subspace learning loss, and can be incorporated with various existing subspace learning methods to obtain different mapping functions {hk}Qk=1 , {gk}Qk=1 . The subsequent section describes a matrix factorization-based implementation that satisfies the first type of loss.

5 Matrix Factorization Implementation

In this section, we present our matrix factorization-based implementation of the general framework, which serves as the foundation for the overall procedure of constructing and utilizing the heterogeneous learnware market.

5.1 Construct the Learnware Market

The market receives N models with raw specifications from developers and submitted models totally come from T different feature spaces. The raw specification implemented by RKME transforms a data set defined on X up t (t ∈ 1, · · · , T) to an element in a particular specification space (RKHS) Ht. Therefore, total N raw specifications {sraw i }Ni=1 locate in T different specification spaces. In order to better organize heterogeneous models to support service of model recommendation and reuse, the market constructs the learnware market by merging T specification spaces to a global one and adjusting raw specifications {sraw i }Ni=1 to specifications {si}Ni=1 .

Concept factorization. As described in the general framework, merging different specification spaces (align different raw specifications) is achieved by subspace learning, we first present the preliminary of a classical matrix factorizationbased subspace learning method called Concept Factorization (CF) [Xu and Gong, 2004; Cai et al., 2010; Wang et al., 2016]. Given a data matrix Z = [z1, · · · , zN] ∈ Rd×n , CF first generates k concepts which are the linear combination of the original data C := ZW= [c1, · · · , ck] ∈ Rd×k , then CF uses concepts to reconstruct the original data by linear combination with the coefficient matrix V. The two coefficient matrices W,V is learned by minimizing the reconstruction error ∥Z-ZWV⊤∥2F and the reconstruction coefficient matrix V is projection of Z in the subspace with dimension k.

Subspace learning. Based on CF, we proposed the following objective function to learn a subspace by only using raw specifications {sraw i := {(βij, zij)}mi j=1 }Ni=1 located in different spaces instead of accessing to raw data of model or collecting extra data across the entire feature space. The objective is optimized over {W(k) }, {V(k) }, {(V∗ )(k) }.

For the sample zij defined on X up ϕi = ×i∈Cϕ Xi with related component feature space index set Cϕi , it can be split into several slices {z(k) ij }k∈Cϕi . Z(k) is the concatenation of all slices on Xk, Γ(k) is a diagonal matrix constructed from weights associated with Z(k) , L(k) is the Laplacian matrix calculated on Z(k) . V(k) is the projection of Z(k) in the subspace and (V∗ )(k) is the intermediate optimization results of V(k) . α, γ are regularizer coefficients. The objective sums the loss of Q component feature spaces, each consists of three items, the first is the reconstruction error of subspace learning, the second is a manifold regularizer which keeps the local structure and the third enforces similarity among the projections of {z(k) ij }k∈Cϕi . The iterative optimization using gradient descent and multiplicative updated rule [F´evotte and Idier, 2011] is described in the Appendix.

Subspace projection and reconstruction. During the step of subspace learning, the base matrix B(k) = Z(k)W(k) of the component feature space Xk is obtained, resulting in the reconstruction function gk : Xsub ↦→Xk given by

Subsequently, the subspace projection functions hk : Xk ↦→ Xsub can be obtained by minv∥z - B(k)v∥2F , which has a closed-form solution given by

Heterogeneous learnware market construction. Based on the aforementioned matrix factorization-based implementation for the general framework, the learnware market can generate mapping functions {hk, gk}Qk=1 from submitted raw specifications sraw i to connect component feature spaces {Xk}Qk=1 to the unified subspace Xsub. For the raw specification of i-th model sraw i := {(βij, zij)}mi j=1 whose component feature space index set is Cϕi , the market transforms it to the specification si = {(βij , vij )}mi j=1 by

The raw specifications {sraw i }ki=1 are located in T different specification spaces initially, but with market merging different specification spaces, all adjusted specifications {si}Ni=1 are located in an identical specification space.

5.2 Exploit the Learnware Market

Learnware recommendation. When the user submits her task requirement sraw 0 := {β0j, z0j}m0 j=1 generated by RKME, the market uses subspace projection functions {hk}Qk=1 to project the requirement to the unified specification space which contains all specifications {si}Ni=1 via Eq. (6) and the projected requirement is s0 := {β0j, v0j}m0 j=1 . The market then calculate the relevance for each learnware by

where Φ0(·) = ∑m0 j=1 β0jk(v0j, ·) is KME of the adjusted requirement s0 and Φi(·) = ∑mi j=1 βijk(vij, ·) is KME of adjusted specification si. The relevance estimation problem Eq. (7) can be solved by quadratic programming [Smola et al., 2007]. With pre-defined threshold th, the learnware market recommends learnwares whose relevance is above the threshold. Furthermore, the market will pass a learnware selector [Wu et al., 2023] and mapping functions {hk, gk}Qk=1 to the user. The learnware selector is used to predict which learnware should each sample use and the selector F(·) is trained by the samples in {si|ωi ≥ th} and the labels are corresponding learnware indices.

Learnware reuse. When the user receives recommended models {fi|ωi ≥ th}, the learnware selector F(·) and mapping functions {hk, gk}Qk=1 , the user can make a prediction via dynamic classifier selection [Zhou, 2012] and feature space transformation. More specifically, the user first projects her task data D0 := {x0j}n0 j=1 to the subspace via Eq. (6) and get Dproj 0 := {v0j}n0 j=1 . The learnware selector predicts on Dproj 0 to decide for each example which model should be used. For example, the sample x0j is predicted to use the learnware fF(v0j), when the model shares the same feature space with x0j, the model can make a prediction directly, otherwise, the user needs to fill up the corresponding missing part by gk(v0j). For example, the model is defined on X1 ×X2 and the instance x0j is defined on X2 ×X3, then the user needs to fill up the data on X1 by g1(v0j).

Algorithm 1 Submitting stage (market construction)

• 1: Each developer trains a model fi and generates the raw specification sraw i on her dataset Di.
• 2: Each developer uploads both model and the raw specification (fi, sraw i ) to the learnware market.
• 3: The learnware market generates 2Q mapping functions {hk, gk}Qk=1 on all feature space components {Xi}Qi=1 based on submitted raw specifications {sraw i }Ni=1 .
• 4: The learnware market uses mapping functions {hk}Qk=1 to generate the specification si for each uploaded model fi based on the raw specification sraw i .
• 5: The heterogeneous learnware market is established as {(fi, si)}Ni=1 .

Algorithm 2 Deploying stage (market exploitation)

• 1: The user generates her requirement of the task sraw 0 and passes it to the market.
• 2: The market uses mapping functions {hk}Qk=1 to postprocess the user requirement and get s0.
• 3: The market recommends models based on the requirement s0 and specifications {si}Ni=1 , it also passes mapping functions {hk, gk}Qk=1 and the model selector F in the subspace.
• 4: The user reuses recommended models on her task via dynamic classifier selection.

5.3 Overall Procedure

In the submitting stage, the market manager receives models from different feature spaces and builds a heterogeneous learnware market by aligning specifications to a common space. In the deploying stage, the user describes her requirement of the task and get help from the market by reusing recommended learnwares. The overall procedure is sketched in Algorithms 1 and 2.

6 Experiments

The experiments 1 consists of two parts, we first illustrate the overall procedure through a toy example for visualizing the superiority of our solution, and then we present performance on several real-world tasks to showcase the effectiveness.

6.1 Toy Example

In the toy example, we assume the overall feature space is split into three components Xall = X1×X2×X3. The overall feature space Xall is R6 and each component Xi is R2 .

In the submitting stage, there are three developers submit their models defined over X1 × X2, X2 × X3, X1 × X3 separately and each model is a 2-class support vector machine using RBF kernel. Raw specifications are illustrated in Figures 3(a), 3(b) and 3(c) by the reduced set (minor weighted samples) and based on which the market generates mapping functions. Then, the market can adjust three specifications located in three spaces to a unified one as Figure 3(d) shows.

1 https://github.com/LAMDA-TP/Heterogeneous-learnwarewithout-auxiliary-data

In the deploying stage, the user possesses the unlabeled data defined over X1×X2 showed in Figures 4(a) and 4(b) and hopes to receive service from the market. The distribution of user's task is a mixture of that of learnware #2 and #3 with equal weights (the mixture is defined over X). The user first generates the task requirement and submit it to the market and then the market projects the requirement in the unified space which contains all specifications as Figure 4(d) shows. After requirement projection, the market is able to calculate the relevance of each learnwares as [0, 0.457, 0.543]. With preset threshold L = 0.1, the learnware market recommends models #2,#3 to the user. To be noticed that the feature space of user's task X1 ×X2 doesn't match that of learnwares #2,#3 (X2 × X3,X1 × X3), the user can reconstruct data on X3 based on the original data and mapping functions as Figure 4(c) shows. After features are complemented, the user reuses recommended models via dynamic classifier selection and the final accuracy is 0.995.

If the market only recommends learnwares which precisely match user's feature space, then the totally irrelevant model #1 is recommended to the user and the final accuracy on user's task is 0, which shows the necessity of exploring learnwares whose feature spaces are not matched with user's task.

6.2 Real-world Tasks

Data set. We conduct empirical experiments on six heterogeneous learnware scenarios involving five real-world tasks: Mfeat [van Breukelen et al., 1998], Anuran [Colonna et al., 2012], Digits [Garris et al., 1997], Kddcup99 [Lippmann et al., 2000] and Covtype [Blackard and Dean, 1999]. Mfeat is a digit recognition data set with six feature spaces, we split it into two parts, generating two separate scenarios. Anuran is used for classifying Anuran sounds. Digits is a collection of handwritten numbers, Kddcup99 is a dataset for network intrusion detection, and Covtype is used for classifying the cover type (the dominant species of trees) in forest patches across the United States. The original datasets of Anuran, Digits, Kddcup99, and Covtype each have a single feature space. We randomly split them into three parts. The information of five processed data set is shown in Table 1.

Contenders. As the heterogeneous learnware problem is a new problem, we first compare with two basic contenders.

• Random: The market randomly selects a model whose feature space is identical with the user's task and the user uses it directly to make a prediction.
• Ensemble: The market selects all models whose feature space are identical with the user's task and the user uses them via average ensemble.

Then we consider other three additional contenders in which model recommendation and reuse rely on specifications.

• MMD: Each model is equipped with the raw specification and the market recommends the learnware with minimum maximum mean discrepancy within models sharing the same feature space with the user's task. The user reuses the model directly [Wu et al., 2023].
• Auxiliary: Each model is equipped with specification generated based on the raw data of the model and the extra auxiliary data across the entire feature space. The user reuses recommended models via dynamic classifier selection [Tan et al., 2022].
• Projection: This method is slightly different from our method. The market recommends learnwares instead of models to the user and the user samples a mimic data set by kernel herding [Chen et al., 2012] for each learnware and uses the learnware to make a prediction, then the user project multiple pseudo-labeled mimic data sets to the subspace and trains a classifier. Finally, the prediction is made on the projected task data with newly trained model.

Experiment setup. The overall feature space is split into three parts Xall = X1 × X2 × X3, we generate 6 learnwares on three feature spaces (X1 × X2,X2 × X3,X1 × X3) and each learnware only classifies a subset of entire classes. The distribution of the user's task is a mixture of several learnwares with equal weights. In our experiment, parameters are set as follows: the reduced set size mi is 10, which is much smaller than the size of original data set ni (mi = O(lnni)). For subspace learning, the trade-off parameters is set as α = 10-5 , γ = 1, the max iteration is t = 500 and the learning rate is η = 10-2 . The dimension of subspace is chosen by cross validation. We test several model types like SVM and random forest. All experiments are repeated 50 times.

User's task performance. Table 2 shows the accuracy of different methods on the user's task, with our method consistently outperforming the others. However, Random, Ensemble, and MMD don't fully leverage the potential of the learnware market as they only recommend learnwares within the same feature space. Among them, Random performs the worst, while MMD achieves better accuracy using distribution information. Compared to these methods, Auxiliary identifies all heterogeneous models but can't reuse models with partially intersected feature spaces. In contrast, both Projection and our method can identify and reuse all heterogeneous models. Projection uses pseudo-labels to reuse models, while we directly use models for predictions. Our method offers a superior approach to reusing models and provides better data privacy protection than Projection.

7 RelatedWork

The learnware paradigm [Zhou, 2016; Zhou and Tan, 2022] devotes to build a model market to help users identify and reuse helpful models for their tasks instead of starting from scratch. When all models share the same feature space, the learnware identification can be conducted by RKME matching and job selector [Wu et al., 2023]. Based on RKME specification, the market can also identify unseen part within user's data and handle the remaining part [Zhang et al., 2021]. When models have different feature spaces, existing study can only initialize the market with the help of extra auxiliary data [Tan et al., 2022].

Domain adaptation [Ben-David et al., 2006] and transfer learning [Pan and Yang, 2009; Ding et al., 2022] transfer the knowledge from the source domain to the target domain. This presupposes a similarity between target and source tasks to avoid negative transfer [Wang et al., 2019]. However, within the learnware paradigm, the user's task may only correlate with a few learnwares in the market, making model identification vital. Model reuse focuses on reusing existing models for a current task without raw data access [Zhao et al., 2020; Ding and Zhou, 2020]. However, it assumes all models are beneficial to the task, which differs from the learnware paradigm where only a few models are useful.

Existing studies on heterogeneous feature spaces including heterogeneous domain adaptation [Duan et al., 2012; Wang and Mahadevan, 2011], heterogeneous transfer learning [Day and Khoshgoftaar, 2017], heterogeneous model reuse [Ye et al., 2018; Ye et al., 2020], etc., generally map different feature spaces to an intermediate subspace. In this process, original data from both domains or co-occurrence data are always necessary for constructing the relationship between different spaces. However, in the learnware paradigm, handling models developed from different feature spaces without auxiliary data becomes feasible due to the existence of RKME specifications associated with each model. Based on the learnware paradigm, we realize to solve the challenge of accommodating, identifying, and reusing heterogeneous models of any type while having no access to original data or extra co-occurrence auxiliary data.

8 Conclusion

This paper presents a general framework for constructing and utilizing the heterogeneous learnware market without relying on additional auxiliary data, while ensuring the privacy of the developers and users. The key idea is to utilize the raw RKME specifications to generate mapping functions that connect different feature spaces to a shared subspace. The generated mapping functions allow for the adjustment of raw specifications during market construction and the handling of missing features when users exploit the market. A compelling issue for future research is how to further adapt mapping functions on user's task for better learnware reuse.

Acknowledgements

This research was supported by NSFC (62250069) and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Authors want to thank Jin-Hui Wu for helpful discussions, and thank reviewers for helpful comments.