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Spectral Regression: A Unified Approach for Sparse Subspace Learning
2007
Seventh IEEE International Conference on Data Mining (ICDM 2007)
Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, information retrieval, and pattern recognition. Some popular methods include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). However, a disadvantage of all these approaches is that the learned projective functions are linear combinations of all the original
doi:10.1109/icdm.2007.89
dblp:conf/icdm/CaiHH07
fatcat:j34dzernwnfhhjf36hd4ssgo5u