Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal locality preserving indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A new approach for face recognition by sketches in photos
Signal Processing
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Interactive cartoon reusing by transfer learning
Signal Processing
Discriminative information preservation for face recognition
Neurocomputing
Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot
Information Sciences: an International Journal
Fast multi-view segment graph kernel for object classification
Signal Processing
Structured light-based shape measurement system
Signal Processing
Multiple linear regression modeling for compositional data
Neurocomputing
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Least square regression (LSR) is popular in pattern classification. Compared against other matrix factorization based methods, it is simple yet efficient. However, LSR ignores unlabeled samples in the training stage, so the regression error could be large when the labeled samples are insufficient. To solve this problem, the Laplacian regularization can be used to penalize LSR. Extensive theoretical and experimental results have confirmed the validity of Laplacian regularized least square (LapRLS). However, multiple hyper-parameters have been introduced to estimate the intrinsic manifold induced by the regularization, and thus the time consuming cross-validation should be applied to tune these parameters. To alleviate this problem, we assume the intrinsic manifold is a linear combination of a given set of known manifolds. By further assuming the priors of the given manifolds are equivalent, we introduce the entropy maximization penalty to automatically learn the linear combination coefficients. The entropy maximization trades the smoothness off the complexity. Therefore, the proposed model enjoys the following advantages: (1) it is able to incorporate both labeled and unlabeled data into training process, (2) it is able to learn the manifold hyper-parameters automatically, and (3) it approximates the true probability distribution with respect to prescribed test data. To test the classification performance of our proposed model, we apply the model on three well-known human face datasets, i.e. FERET, ORL, and YALE. Experimental results on these three face datasets suggest the effectiveness and the efficiency of the new model compared against the traditional LSR and the Laplacian regularized least squares.