A scalable two-stage approach for a class of dimensionality reduction techniques
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient face recognition using tensor subspace regression
Neurocomputing
Face recognition using multilinear manifold analysis of local descriptors
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Robust spectral regression for face recognition
Neurocomputing
| Hi-index | 0.00 |
Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a data affinity (i.e., item-item similarity) matrix to reveal the low dimensional structure in the high dimensional data. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms only provide the embedding results of training samples. There are many extensions of these approaches which try to solve the out-of-sample extension problem by seeking an embedding function in reproducing kernel Hilbert space. However, a disadvantage of all these approaches is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this thesis, we introduce a novel dimensionality reduction framework, called Spectral Regression (SR). SR casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression as a building block, different kinds of regularizers can be naturally incorporated into our framework which makes it more flexible. SR can be performed in supervised, unsupervised and semi-supervised situation. It can make efficient use of both labeled and unlabeled points to discover the intrinsic discriminant structure in the data. We have applied our algorithms to several real world applications, e.g. face analysis, document representation and content-based image retrieval.