Sparse subspace clustering with linear subspace-neighborhood-preserving data embedding

Jwo Yuh Wu, Liang Chi Huang, Wen Hsuan Li, Hau Hsiang Chan, Chun Hung Liu, Rung Hung Gau

研究成果: Conference contribution同行評審

摘要

Data dimensionality reduction via linear embedding is a typical approach to economizing the computational cost of machine learning systems. In the context of sparse subspace clustering (SSC), this paper proposes a two-step neighbor identification scheme using linear neighborhood-preserving embedding. In the first step, a quadratically-constrained l1 -minimization algorithm is solved for acquiring the side subspace neighborhood information, whereby a linear neighborhood-preserving embedding is designed accordingly. In the second step, a LASSO sparse regression algorithm is conducted for neighbor identification using the dimensionality-reduced data. The proposed embedding design explicitly takes into account the subspace neighborhood structure of the given data set. Computer simulations using real human face data show that the proposed embedding not only outperforms other existing dimensionality-reduction schemes but also improves the global data clustering accuracy when compared to the baseline solution without data compression.

原文English
主出版物標題2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
發行者IEEE Computer Society
ISBN(電子)9781728119465
DOIs
出版狀態Published - 六月 2020
事件11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020 - Hangzhou, China
持續時間: 8 六月 202011 六月 2020

出版系列

名字Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
2020-June
ISSN(電子)2151-870X

Conference

Conference11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
國家China
城市Hangzhou
期間8/06/2011/06/20

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