Sixth International Conference on Data Mining (ICDM'06) (2006)
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.7
Jun Yan , Microsoft Research Asia, China
Ning Liu , Microsoft Research Asia, China
Benyu Zhang , Microsoft Research Asia, China
Qiang Yang , Hong Kong University of Science and Technology, Hong Kong
Shuicheng Yan , University of Illinois at Urbana Champaign, USA
Zheng Chen , Microsoft Research Asia, China
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as Principal Component Analysis (PCA) do not make use of the class information, and Linear Discriminant Analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as Supervised Kampong Measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, Information Gain (IG).
B. Zhang, N. Liu, J. Yan, Z. Chen, S. Yan and Q. Yang, "A Novel Scalable Algorithm for Supervised Subspace Learning," Sixth International Conference on Data Mining (ICDM'06)(ICDM), Hong Kong, 2006, pp. 721-730.