Error-Robust Multi-View Clustering

TL;DR

This paper introduces EMVC, a novel multi-view clustering method that explicitly models and handles various data errors, improving clustering robustness in noisy multi-source data environments.

Contribution

It proposes a new Markov chain-based approach with structured error modeling and an efficient optimization algorithm, addressing all common error types in multi-view clustering.

Findings

Outperforms baseline methods on synthetic and real datasets

Demonstrates robustness against different error types

Provides a convergent optimization algorithm

Abstract

In the era of big data, data may come from multiple sources, known as multi-view data. Multi-view clustering aims at generating better clusters by exploiting complementary and consistent information from multiple views rather than relying on the individual view. Due to inevitable system errors caused by data-captured sensors or others, the data in each view may be erroneous. Various types of errors behave differently and inconsistently in each view. More precisely, error could exhibit as noise and corruptions in reality. Unfortunately, none of the existing multi-view clustering approaches handle all of these error types. Consequently, their clustering performance is dramatically degraded. In this paper, we propose a novel Markov chain method for Error-Robust Multi-View Clustering (EMVC). By decomposing each view into a shared transition probability matrix and error matrix and imposing structured sparsity-inducing norms on error matrices, we characterize and handle typical types of errors explicitly. To solve the challenging optimization problem, we propose a new efficient algorithm based on Augmented Lagrangian Multipliers and prove its convergence rigorously. Experimental results on various synthetic and real-world datasets show the superiority of the proposed EMVC method over the baseline methods and its robustness against different types of errors.