Semi-supervised multi-view concept decomposition

TL;DR

This paper introduces SMVCF, a semi-supervised multi-view concept factorization model that enhances multi-view clustering by integrating label propagation, manifold learning, and adaptive view weighting, demonstrating superior performance on diverse datasets.

Contribution

The paper proposes a novel semi-supervised multi-view concept factorization model that extends traditional CF to multiple views and incorporates label information and manifold learning.

Findings

SMVCF outperforms existing methods in multi-view clustering tasks.

The adaptive weighting improves the integration of multiple views.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

Concept Factorization (CF), as a novel paradigm of representation learning, has demonstrated superior performance in multi-view clustering tasks. It overcomes limitations such as the non-negativity constraint imposed by traditional matrix factorization methods and leverages kernel methods to learn latent representations that capture the underlying structure of the data, thereby improving data representation. However, existing multi-view concept factorization methods fail to consider the limited labeled information inherent in real-world multi-view data. This often leads to significant performance loss. To overcome these limitations, we propose a novel semi-supervised multi-view concept factorization model, named SMVCF. In the SMVCF model, we first extend the conventional single-view CF to a multi-view version, enabling more effective exploration of complementary information across multiple views. We then integrate multi-view CF, label propagation, and manifold learning into a unified framework to leverage and incorporate valuable information present in the data. Additionally, an adaptive weight vector is introduced to balance the importance of different views in the clustering process. We further develop targeted optimization methods specifically tailored for the SMVCF model. Finally, we conduct extensive experiments on four diverse datasets with varying label ratios to evaluate the performance of SMVCF. The experimental results demonstrate the effectiveness and superiority of our proposed approach in multi-view clustering tasks.