Identifying critical variables of principal components for unsupervised feature selection
作者： K.Z. Mao
DOI: 10.1109/TSMCB.2004.843269
关键词:
摘要: Principal components analysis (PCA) is probably the best-known approach to unsupervised dimensionality reduction. However, axes of the lower-dimensional space, ie, principal …
sci-hub.se 参考文章(22)
W. J. Krzanowski, Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components Applied Statistics. ,vol. 36, pp. 22- 33 ,(1987) , 10.2307/2347842
I. T. Jolliffe, Discarding Variables in a Principal Component Analysis. Ii: Real Data Journal of The Royal Statistical Society Series C-applied Statistics. ,vol. 22, pp. 21- 31 ,(1973) , 10.2307/2346300
Carla E. Brodley, Jennifer G. Dy, Feature Subset Selection and Order Identification for Unsupervised Learning international conference on machine learning. pp. 247- 254 ,(2000)
Josef Kittler, Pierre A. Devijver, Pattern recognition : a statistical approach Prentice/Hall International. ,(1982)
Luis Talavera, Feature Selection as a Preprocessing Step for Hierarchical Clustering international conference on machine learning. pp. 389- 397 ,(1999)
Luis Talavera, Dependency-based feature selection for clustering symbolic data intelligent data analysis. ,vol. 4, pp. 19- 28 ,(2000) , 10.3233/IDA-2000-4103
P. Pudil, J. Novovičová, J. Kittler, Floating search methods in feature selection Pattern Recognition Letters. ,vol. 15, pp. 1119- 1125 ,(1994) , 10.1016/0167-8655(94)90127-9
Ron Kohavi, George H. John, Wrappers for feature subset selection Artificial Intelligence. ,vol. 97, pp. 273- 324 ,(1997) , 10.1016/S0004-3702(97)00043-X
P. Pudil, J. Hovovicova, Novel methods for subset selection with respect to problem knowledge IEEE Intelligent Systems & Their Applications. ,vol. 13, pp. 66- 74 ,(1998) , 10.1109/5254.671094
C. L. Blake, UCI Repository of machine learning databases www.ics.uci.edu/〜mlearn/MLRepository.html. ,(1998)