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  • Peng_Pavlidis2022

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Weighted Sparse Simplex Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>11/02/2022
<mark>Journal</mark>Data Mining and Knowledge Discovery
Publication StatusE-pub ahead of print
Early online date11/02/22
<mark>Original language</mark>English

Abstract

Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based subspace clustering algorithm that seeks to represent each point as a sparse convex combination of a few nearby points. We then extend the algorithm to a constrained clustering and active learning framework. Our motivation for developing such a framework stems from the fact that typically either a small amount of labelled data are available in advance; or it is possible to label some points at a cost. The latter scenario is typically encountered in the process of validating a cluster assignment. Extensive experiments on simulated and real datasets show that the proposed approach is effective and competitive with state-of-the-art methods.