Deep Multiview Collaborative Clustering.

Area of Science:

• Machine Learning
• Computer Vision
• Data Science

Background:

• Clustering methods are gaining significant attention in machine learning and computer vision.
• Traditional multiview clustering methods often fail to consider representation diversity and are computationally expensive.
• Existing approaches typically learn a common latent space and use K-means, which is time and space consuming.

Purpose of the Study:

• To propose a novel end-to-end deep multiview clustering model.
• To address the limitations of traditional methods in handling multiview data diversity and computational cost.
• To directly predict clustering results using a collaborative learning framework.

Main Methods:

• Utilizing multiple autoencoder networks to embed multi-view data into various latent spaces.
• Employing a heterogeneous graph learning module for adaptive fusion of latent representations with sample-specific view weights.
• Implementing intra-view collaborative learning for optimizing single-view clustering and enhancing latent representations.
• Applying inter-view collaborative learning to leverage complementary information and promote consistent cluster structures.

Main Results:

• The proposed model significantly outperforms several state-of-the-art clustering approaches on multiple datasets.
• Demonstrated effectiveness in handling multiview data by adaptively fusing latent representations.
• Achieved more discriminative latent representations through intra-view collaborative learning.

Conclusions:

• The novel deep multiview clustering model with collaborative learning offers a more efficient and effective solution for clustering diverse data.
• The adaptive fusion of latent representations and collaborative learning strategies enhance clustering performance.
• This approach provides a promising direction for real-world multiview clustering applications.