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Subspace Clustering Using Matrix Factorization

Publication Type : Conference Paper

Publisher : Lecture Notes in Electrical Engineeringthis link is disabled, 2021, 735 LNEE, pp. 203–215

Authors : Sandhya Harikumar, Shilpa Joseph

Source : Lecture Notes in Electrical Engineeringthis link is disabled, 2021, 735 LNEE, pp. 203–215

Keywords : High-dimensional data, Subspace clustering , Matrix factorization, Singular value decomposition

Campus : Amritapuri

School : Department of Computer Science and Engineering

Department : Computer Science

Year : 2021

Abstract : High-dimensional data suffers from the curse of dimensionality and sparsity problems. Since all samples seem equidistant from each other in high-dimensional space, low-dimensional structures need to be found for cluster formation. This paper proposes a top-down approach for subspace clustering called projective clustering to identify clusters in low-dimensional subspaces using best low-rank matrix factorization strategy, singular value decomposition. The advantages of this approach are twofold. First is to obtain multiple low-dimensional substructures using the best low-rank approximation, thereby reducing the storage requirements. Second is the usage of the obtained projective clusters to retrieve approximate results of a given query in time-efficient manner. Experimentation on six real-world datasets proves the feasibility of our model for approximate information retrieval.

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