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 language||English (US)|
|Number of pages||10|
|Journal||Proceedings - IEEE International Conference on Data Mining, ICDM|
|State||Published - Jan 26 2015|
|Event||14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China|
Duration: Dec 14 2014 → Dec 17 2014
All Science Journal Classification (ASJC) codes