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

Xinxin Wang

Abstract:

Incomplete multi-view clustering presents significant challenges
due to missing views. Although many existing graphbased
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 rotationinvariant
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-ofthe-
art methods.