Tensor-based Low-dimensional Representation Learning for Multi-view Clustering.

Area of Science:

• Machine Learning
• Data Science
• Artificial Intelligence

Background:

• Multi-view clustering leverages diverse data sources for richer representations.
• A key challenge is effectively representing heterogeneous, high-dimensional multi-view data.
• Existing methods struggle to sufficiently capture comprehensive multi-view information.

Purpose of the Study:

• To introduce a novel tensor-based Representation Learning method for Multi-view Clustering (tRLMvC).
• To unify heterogeneous and high-dimensional multi-view feature spaces into a low-dimensional shared latent space.
• To improve multi-view clustering performance by enhancing data representation.

Main Methods:

• Representing multi-view data as a third-order tensor.
• Utilizing a sparse t-linear combination with t-product to form a self-expressive tensor.
• Employing iterative Tucker decomposition on the self-expressive tensor for low-dimensional representation.
• Enhancing the interaction between self-expressive tensor learning and its factorization.

Main Results:

• The proposed tRLMvC method effectively unifies multi-view data into a shared latent space.
• tRLMvC demonstrates superior performance compared to state-of-the-art methods on eight benchmark datasets.
• Experimental results show significant improvements in various clustering evaluation metrics.

Conclusions:

• The tensor-based approach provides a powerful framework for multi-view data representation learning.
• tRLMvC offers an effective solution for improving multi-view clustering performance.
• The iterative enhancement of self-expressive tensor learning and factorization is crucial for generating effective representations.