Structured Optimal Graph-Based Clustering With Flexible Embedding.

Area of Science:

• Data Science
• Machine Learning
• Computational Statistics

Background:

• High-dimensional data presents challenges due to its inherent complexity.
• Coclustering methods leverage co-occurring structures in samples and features but often require postprocessing and struggle with dimensionality.
• Existing methods typically cluster graphs in the original data matrix, lacking explicit cluster structures in the output affinity graph.

Purpose of the Study:

• To develop a novel coclustering method that overcomes limitations of existing approaches.
• To create a method that outputs an affinity graph with an explicit cluster structure, eliminating the need for postprocessing.
• To enable effective clustering of high-dimensional data by learning optimal projection directions.

Main Methods:

• The proposed method, structured optimal graph-based clustering with flexible embedding (SOGFE), integrates flexible manifold embedding theory with bipartite spectral graph partitioning.
• SOGFE learns an affinity graph with an optimal and explicit clustering structure directly.
• The method incorporates a mechanism to learn a suitable projection direction for mapping data into a lower-dimensional subspace.

Main Results:

• Extensive experiments on synthetic and benchmark datasets demonstrate the effectiveness of SOGFE.
• SOGFE shows superiority over existing methods in coclustering high-dimensional data.
• The method exhibits robustness and a strong ability to select appropriate projection directions.

Conclusions:

• SOGFE offers an effective and efficient solution for coclustering high-dimensional data.
• The method's ability to directly output a structured affinity graph and learn projection directions simplifies the clustering workflow.
• SOGFE represents a significant advancement in coclustering techniques for complex datasets.