Discriminative Projected Clustering via Unsupervised LDA

Feiping Nie, Xia Dong, Zhanxuan Hu, Rong Wang, Xuelong Li

科研成果: 期刊稿件文章同行评审

9 引用 (Scopus)

摘要

This work focuses on the projected clustering problem. Specifically, an efficient and parameter-free clustering model, named discriminative projected clustering (DPC), is proposed for simultaneously low-dimensional and discriminative projection learning and clustering, from the perspective of least squares regression. The proposed DPC, a constrained regression model, aims at finding both a transformation matrix and a binary indicator matrix to minimize the sum-of-squares error. Theoretically, a significant conclusion is drawn and used to reveal the connection between DPC and linear discriminant analysis (LDA). Experimentally, experiments are conducted on both toy and real-world data to validate the effectiveness and efficiency of DPC; experiments are also conducted on hyperspectral images to further verify its practicability in real-world applications. Experimental results demonstrate that DPC achieves comparable or superior results to some state-of-the-art clustering methods.

源语言英语
页(从-至)9466-9480
页数15
期刊IEEE Transactions on Neural Networks and Learning Systems
34
11
DOI
出版状态已出版 - 1 11月 2023

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