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Discriminating Tensor Spectral Clustering for High-Dimension-Low-Sample-Size Data.

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|July 9, 2024

Summary

This summary is machine-generated.

This study introduces discriminating tensor spectral clustering (DTSC) for high-dimension-low-sample-size data. DTSC improves clustering by using a novel affinity tensor that better differentiates samples, outperforming existing methods.

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Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

Traditional spectral clustering (SC) uses pairwise data similarities.
Tensor spectral clustering (TSC) explores multiwise similarities for improved performance.
TSC's effectiveness depends on multiwise similarity design, especially for high-dimension-low-sample-size (HDLSS) data.

Purpose of the Study:

To propose a discriminating TSC (DTSC) method tailored for HDLSS data.
To enhance clustering performance by addressing limitations in TSC for HDLSS datasets.
To develop a robust clustering approach for complex, high-dimensional data.

Main Methods:

Developed a discriminating affinity tensor using anchor-based distance for pair-to-pair similarities.
Employed HDLSS asymptotic analysis to validate the affinity tensor's properties.
Implemented DTSC for robust data clustering on various datasets.

Main Results:

The proposed affinity tensor effectively differentiates samples from different clusters in HDLSS settings.
DTSC demonstrates improved clustering performance and robustness compared to baseline methods.
Experimental results on synthetic and benchmark datasets confirm DTSC's efficacy.

Conclusions:

DTSC offers a significant advancement in clustering HDLSS data.
The discriminating affinity tensor is key to DTSC's success in high-dimensional spaces.
DTSC provides a robust and effective solution for challenging clustering tasks.