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CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Guo-Sen Xie

TL;DR

A novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), is proposed, which captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework.

Abstract

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.

CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

TL;DR

A novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), is proposed, which captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework.

Abstract

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.
Paper Structure (16 sections, 13 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 13 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Kmeans Clustering
  3. The Proposed Method
  4. Problem Statement
  5. CDIMC-net
  6. Pre-training Network
  7. Fine-tuning and Clustering
  8. Optimization
  9. Implementation for IMC
  10. Experiment
  11. Experimental Settings
  12. Experimental Results and Analysis
  13. Parameter Analysis
  14. Component Analysis
  15. Convergence Analysis
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The proposed CDIMC-net for incomplete multi-view clustering.
  • Figure 2: ACC (%) v.s. $\alpha$ and learning rate (lr) of CDIMC-net on (a) Handwritten and (b) BDGP databases with a missing-view rate of 10%.
  • Figure 3: ACC (%) of CDIMC-net and its three degenerate models on (a) Handwritten and (b) BDGP databases.
  • Figure 4: Loss v.s. iterations of CDIMC-net on (a) Handwritten and (b) BDGP databases with a missing-view rate of 10%.