Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering.

Area of Science:

• Machine Learning
• Data Mining
• Computer Science

Background:

• Graph-based clustering partitions data using similarity graphs, crucial for various tasks.
• Producing high-quality similarity graphs is challenging due to data noise and outliers.

Purpose of the Study:

• To propose a robust rank constrained sparse learning (RRCSL) method for improved graph-based clustering.
• To enhance the quality of similarity graphs in the presence of noise and outliers.

Main Methods:

• Utilized L2,1-norm in sparse representation for robust optimal graph learning.
• Incorporated a rank constraint for direct use of the learned graph as a cluster indicator.
• Preserved data structure by searching the graph within its neighborhood.

Main Results:

• The RRCSL method demonstrated superior performance and robustness on synthetic and real-world datasets.
• The learned graph directly indicated clusters, eliminating the need for postprocessing.
• The framework was successfully extended from single-view to multi-view clustering.

Conclusions:

• The proposed RRCSL method offers a robust and effective approach to graph-based clustering.
• The method excels in handling noisy data and preserving data structure.
• RRCSL provides a unified framework for both single-view and multi-view clustering tasks.