Multiview Subspace Clustering via Tensorial t-Product Representation

Area of Science:

• Data Science
• Machine Learning
• Computer Vision

Background:

• Multiview data offers complementary information beneficial for tasks like clustering.
• Existing multiview subspace clustering methods often overlook high-order statistics by focusing on pairwise correlations.
• This limitation compromises the performance of multiview data clustering.

Purpose of the Study:

• To propose a novel multiview clustering method that effectively utilizes high-order statistics from all views.
• To address the limitations of existing methods in capturing comprehensive information from multiview data.
• To improve the performance of multiview subspace clustering.

Main Methods:

• A novel tensor construction method is proposed to organize multiview data for tensor-tensor product application.
• Multiview data is represented using a t-linear combination with sparse and low-rank penalties via self-expressiveness.
• The method leverages t-product in the third-order tensor space to capture higher-order statistics.

Main Results:

• The proposed method demonstrates superior performance compared to state-of-the-art techniques.
• Experiments were conducted on diverse datasets including face, object, digital image, and text data.
• The method effectively captures high-order statistics, leading to improved clustering accuracy.

Conclusions:

• The novel tensor-based multiview clustering method effectively captures high-order statistics, outperforming existing approaches.
• This method offers a significant advancement in analyzing and clustering complex multiview data.
• The approach shows promise for various applications requiring robust multiview data analysis.