Fast Continual Multi-View Clustering with Incomplete Views

TL;DR

This paper introduces FCMVC-IV, a fast and efficient method for multi-view clustering that handles incomplete and continually arriving data views without storing all previous data, demonstrating superior performance.

Contribution

It proposes a novel incremental clustering algorithm that updates knowledge with incomplete views, using a consensus matrix and indicator matrices, with proven linear complexity.

Findings

Outperforms existing methods on various datasets

Maintains linear complexity in processing

Effectively handles incomplete and continual data

Abstract

Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). In specific, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works are proposed to handle it, but all fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it. Specifically, it maintains a consensus coefficient matrix and updates knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. Besides, we design a three-step iterative algorithm to solve the resultant problem in linear complexity with proven convergence. Comprehensive experiments on various datasets show the superiority of FCMVC-IV.