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

Peng, Hankui and Pavlidis, Nicos (2022) Weighted Sparse Simplex Representation : A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning. Data Mining and Knowledge Discovery, 36 (3). 958–986. ISSN 1384-5810

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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.