Multi-view Fuzzy Representation Learning with Rules based Model

by   Wei Zhang, et al.

Unsupervised multi-view representation learning has been extensively studied for mining multi-view data. However, some critical challenges remain. On the one hand, the existing methods cannot explore multi-view data comprehensively since they usually learn a common representation between views, given that multi-view data contains both the common information between views and the specific information within each view. On the other hand, to mine the nonlinear relationship between data, kernel or neural network methods are commonly used for multi-view representation learning. However, these methods are lacking in interpretability. To this end, this paper proposes a new multi-view fuzzy representation learning method based on the interpretable Takagi-Sugeno-Kang (TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation learning from two aspects. First, multi-view data are transformed into a high-dimensional fuzzy feature space, while the common information between views and specific information of each view are explored simultaneously. Second, a new regularization method based on L_(2,1)-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph. Finally, extensive experiments on many benchmark multi-view datasets are conducted to validate the superiority of the proposed method.


page 1

page 9

page 13


Dual Representation Learning for One-Step Clustering of Multi-View Data

Multi-view data are commonly encountered in data mining applications. Ef...

Learning Robust Representations via Multi-View Information Bottleneck

The information bottleneck principle provides an information-theoretic m...

Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians

Multi-view subspace learning (MSL) aims to find a low-dimensional subspa...

Transfer Representation Learning with TSK Fuzzy System

Transfer learning can address the learning tasks of unlabeled data in th...

Semantic Snapping for Guided Multi-View Visualization Design

Visual information displays are typically composed of multiple visualiza...

Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning

Job Title Benchmarking (JTB) aims at matching job titles with similar ex...

Disentangling Multi-view Representations Beyond Inductive Bias

Multi-view (or -modality) representation learning aims to understand the...

Please sign up or login with your details

Forgot password? Click here to reset