Abstract

In recent years, subspace clustering has found many practical use cases which include, for example, image segmentation, motion segmentation, and facial clustering. The image and video data that is common to these types of applications often has high dimensionality. Rather than viewing high dimensionality as a drawback, we propose a novel algorithm for subspace clustering that takes advantage of the high dimensional nature of such data. We call this algorithm LP1-PCA Spectral Clustering. Specifically, we introduce a concept that we call cluster-ID sparsity, and we propose an algorithm called LP1-PCA to attain this in low data dimensions. Our novel LP1-PCA algorithm is simple to implement and typically converges after only a few iterations. Conditions for which our algorithm performs well are discussed both theoretically and empirically, and we show that our method often attains superior clustering performance when compared to other common clustering algorithms on synthetic and real world datasets.

Library of Congress Subject Headings

Cluster analysis; Data mining; Algorithms; Principal components analysis

Publication Date

5-27-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Panos P. Markopoulos

Advisor/Committee Member

Sohail A. Dianat

Advisor/Committee Member

Andreas Savakis

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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