Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Theory (North-Holland mathematics studies)
Matching Theory (North-Holland mathematics studies)
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Generalized spectral bounds for sparse LDA
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond sparsity: The role of L1-optimizer in pattern classification
Pattern Recognition
Coarse-to-fine classification via parametric and nonparametric models for computer-aided diagnosis
Proceedings of the 20th ACM international conference on Information and knowledge management
Structured sparse linear graph embedding
Neural Networks
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Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality reduction framework. However, these algorithms yield projective maps which are linear combination of all the original features. The results are difficult to be interpreted psychologically and physiologically. This paper presents a novel technique for learning a sparse projection over graphs. The data in the reduced subspace is represented as a linear combination of a subset of the most relevant features. Comparing to PCA and LDA, the results obtained by sparse projection are often easier to be interpreted. Our algorithm is based on a graph embedding model, which encodes the discriminating and geometrical structure in terms of the data affinity. Once the embedding results are obtained, we then apply regularized regression for learning a set of sparse basis functions. Specifically, by using L1-norm regularizer (e.g. lasso), the sparse projections can be efficiently computed. Experimental results on two document databases demonstrate the effectiveness of our method.