Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering

TL;DR

This paper introduces RISE, a scalable and efficient spectral embedding method for incomplete multi-view clustering that is rotation-invariant and addresses existing limitations in data recovery and computational complexity.

Contribution

The paper proposes a novel rotation-invariant spectral embedding approach with a fast optimization algorithm for scalable incomplete multi-view clustering.

Findings

Outperforms state-of-the-art methods in effectiveness

Demonstrates high scalability and efficiency

Achieves rotation-invariance in spectral embeddings

Abstract

Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.