Deep Multi-View Clustering With Meta Information Compression
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel meta-learning approach for multi-view clustering, learning compact representations with minimal redundancy. The method enhances clustering performance by balancing complementary and consistent information across different data views.
Area Of Science
- Machine Learning
- Data Mining
- Artificial Intelligence
Background
- Multi-view clustering methods utilize data consistency and complementarity for sample partitioning.
- Existing deep learning approaches struggle to balance complementary information with essential details, leading to redundancy or information loss.
Purpose Of The Study
- To develop a novel meta-learning based method for learning clustering-friendly representations with minimal redundancy.
- To address the limitations of existing deep learning methods in multi-view clustering.
Main Methods
- A meta-learning framework employing bi-level optimization for feature embedding and an information compressor.
- Training an information compressor to create compact representations with minimized redundancy.
- Implementing a semantic puzzle mechanism to integrate semantic fragments and enhance discriminative power.
Main Results
- The proposed method effectively learns key semantics with reduced redundancy.
- The semantic puzzle mechanism successfully complements semantic fragments, creating a consensus representation.
- Extensive experiments demonstrated significant performance improvements over state-of-the-art methods on various datasets.
Conclusions
- The novel meta-learning approach offers a superior strategy for multi-view clustering.
- The method effectively overcomes the dilemma of information redundancy versus loss in deep clustering.
- The learned representations exhibit strong discriminative power, leading to enhanced clustering accuracy.
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