Transfer Representation Learning with TSK Fuzzy System

by   Peng Xu, et al.

Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issue in transfer learning is to learn a shared feature space in where the distributions of the data from two domains are matched. This learning process can be named as transfer representation learning (TRL). The feature transformation methods are crucial to ensure the success of TRL. The most commonly used feature transformation method in TRL is kernel-based nonlinear mapping to the high-dimensional space followed by linear dimensionality reduction. But the kernel functions are lack of interpretability and are difficult to be selected. To this end, the TSK fuzzy system (TSK-FS) is combined with transfer learning and a more intuitive and interpretable modeling method, called transfer representation learning with TSK-FS (TRL-TSK-FS) is proposed in this paper. Specifically, TRL-TSK-FS realizes TRL from two aspects. On one hand, the data in the source and target domains are transformed into the fuzzy feature space in which the distribution distance of the data between two domains is min-imized. On the other hand, discriminant information and geo-metric properties of the data are preserved by linear discriminant analysis and principal component analysis. In addition, another advantage arises with the proposed method, that is, the nonlinear transformation is realized by constructing fuzzy mapping with the antecedent part of the TSK-FS instead of kernel functions which are difficult to be selected. Extensive experiments are conducted on the text and image datasets. The results obviously show the superiority of the proposed method.


page 1

page 4

page 8

page 11


Multi-view Fuzzy Representation Learning with Rules based Model

Unsupervised multi-view representation learning has been extensively stu...

Class Mean Vector Component and Discriminant Analysis for Kernel Subspace Learning

The kernel matrix used in kernel methods encodes all the information req...

Multi-Relevance Transfer Learning

Transfer learning aims to faciliate learning tasks in a label-scarce tar...

Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN

Transfer learning of StyleGAN has recently shown great potential to solv...

Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain

The goal of transfer learning is to improve the performance of target le...

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation

Consider the problem of improving the estimation of conditional average ...

Transfer Learning using Representation Learning in Massive Online Open Courses

In MOOCs predictive models of student behavior support many aspects of l...

Please sign up or login with your details

Forgot password? Click here to reset