Latent Complete Row Space Recovery for Multi-view Subspace Clustering

TL;DR

This paper introduces LCRSR, a scalable multi-view subspace clustering method that recovers the latent row space directly, avoiding affinity matrix construction and improving efficiency and accuracy.

Contribution

LCRSR is a novel approach that recovers the complete latent row space for multi-view clustering, eliminating the need for affinity matrix construction and enhancing scalability.

Findings

LCRSR effectively clusters multi-view data with high accuracy.

The method is computationally efficient and scalable to large datasets.

LCRSR outperforms existing methods in background subtraction tasks.

Abstract

Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention. Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views. The affinity construction process is time-consuming, and the combined affinity matrix is not guaranteed to reflect the whole true subspace structure. To overcome these issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed. Concretely, LCRSR is based on the assumption that the multi-view observations are generated from an underlying latent representation, which is further assumed to collect the authentic samples drawn exactly from multiple subspaces. LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions. LCRSR does not involve the graph construction procedure and is solved with an efficient and convergent algorithm, thereby being more scalable to large-scale datasets. The effectiveness and efficiency of LCRSR are validated by clustering various kinds of multi-view data and illustrated in the background subtraction task.