Noise-Resistant Unsupervised Feature Selection via Multi-perspective Correlations

Hao Huang, Shinjae Yoo, Dantong Yu, Hong Qin

Abstract: 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.