A General Framework for Dimensionality Reduction of K-Means Clustering

Tong Wu (), Yanni Xiao (), Muhan Guo () and Feiping Nie ()
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Tong Wu: Xi’an Jiaotong University
Yanni Xiao: Xi’an Jiaotong University
Muhan Guo: Northwestern Polytechnical University
Feiping Nie: Northwestern Polytechnical University

Journal of Classification, 2020, vol. 37, issue 3, No 6, 616-631

Abstract: Abstract Dimensionality reduction plays an important role in many machine learning and pattern recognition applications. Linear discriminant analysis (LDA) is the most popular supervised dimensionality reduction technique which searches for the projection matrix that makes the data points of different classes to be far from each other while requiring data points of the same class to be close to each other. In this paper, trace ratio LDA is combined with K-means clustering into a unified framework, in which K-means clustering is employed to generate class labels for unlabeled data and LDA is used to investigate low-dimensional representation of data. Therefore, by combining the subspace clustering with dimensionality reduction together, the optimal subspace can be obtained. Differing from other existing dimensionality reduction methods, our novel framework is suitable for different scenarios: supervised, semi-supervised, and unsupervised dimensionality reduction cases. Experimental results on benchmark datasets validate the effectiveness and superiority of our algorithm compared with other relevant techniques.

Keywords: Dimensionality reduction; Trace ratio LDA; K-means clustering (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00357-019-09342-4

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