SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Misalignment-robust face recognition
IEEE Transactions on Image Processing
Feature extraction by learning Lorentzian metric tensor and its extensions
Pattern Recognition
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized sparse classifiers for decoding cognitive states in fMRI
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Speed up kernel discriminant analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Generalized sparse regularization with application to fMRI brain decoding
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Linear discriminant dimensionality reduction
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Structured sparse linear graph embedding
Neural Networks
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Joint feature selection and subspace learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Matched signal detection on graphs: theory and application to brain network classification
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Joint clustering and feature selection
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Engineering Applications of Artificial Intelligence
Joint Laplacian feature weights learning
Pattern Recognition
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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 features, 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 L1-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.