Spectral Regression: A Unified Approach for Sparse Subspace Learning

Deng Cai, Xiaofei He, Jiawei Han
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
more » ... s, thus it is often difficult to interpret the results. In this paper, we propose a novel dimensionality reduction framework, called Unified Sparse Subspace Learning (USSL), for learning sparse projections. USSL casts the problem of learning the projective functions into a regression framework, which facilitates the use of different kinds of regularizers. By using a L 1 -norm regularizer (lasso), the sparse projections can be efficiently computed. Experimental results on real world classification and clustering problems demonstrate the effectiveness of our method.
doi:10.1109/icdm.2007.89 dblp:conf/icdm/CaiHH07 fatcat:j34dzernwnfhhjf36hd4ssgo5u