Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities

TL;DR

This paper surveys recent advances in error-robust multi-view clustering, addressing challenges posed by data errors and exploring various methodological approaches to improve clustering performance.

Contribution

It provides a comprehensive review of recent methods for error-robust multi-view clustering, categorizing approaches and highlighting future research directions.

Findings

Summarizes five main categories of error-robust clustering methods.

Identifies key challenges in handling errors in multi-view data.

Suggests future research opportunities in the field.

Abstract

With recent advances in data collection from multiple sources, multi-view data has received significant attention. In multi-view data, each view represents a different perspective of data. Since label information is often expensive to acquire, multi-view clustering has gained growing interest, which aims to obtain better clustering solution by exploiting complementary and consistent information across all views rather than only using an individual view. Due to inevitable sensor failures, data in each view may contain error. Error often exhibits as noise or feature-specific corruptions or outliers. Multi-view data may contain any or combination of these error types. Blindly clustering multi-view data i.e., without considering possible error in view(s) could significantly degrade the performance. The goal of error-robust multi-view clustering is to obtain useful outcome even if the multi-view data is corrupted. Existing error-robust multi-view clustering approaches with explicit error removal formulation can be structured into five broad research categories - sparsity norm based approaches, graph based methods, subspace based learning approaches, deep learning based methods and hybrid approaches, this survey summarizes and reviews recent advances in error-robust clustering for multi-view data. Finally, we highlight the challenges and provide future research opportunities.