Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations

Hao Huang, Shinjae Yoo, Dantong Yu, Hong Qin

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Unsupervised feature selection is an important issue for high dimensional dataset analysis. However popular methods are susceptible to noisy instances (observations) or noisy features. We propose a noise-resistant feature selection algorithm by capturing multi-perspective correlations. Our proposed approach, called Noise-Resistant Unsupervised Feature Selection (NRFS), is based on multi-perspective correlation that reflects the importance of feature with respect to noise-resistant representative instances and various global trends from spectral decomposition. In this way, the model concisely captures a wide variety of local patterns. Experimental results demonstrate the effectiveness of our algorithm.

Original languageEnglish (US)
Article number7023338
Pages (from-to)210-219
Number of pages10
JournalProceedings - IEEE International Conference on Data Mining, ICDM
Issue numberJanuary
StatePublished - Jan 26 2015
Externally publishedYes
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

All Science Journal Classification (ASJC) codes

  • General Engineering


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