Computer
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Top 10 algorithms in data mining
Knowledge and Information Systems
Feature selection with dynamic mutual information
Pattern Recognition
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Graph-optimized locality preserving projections
Pattern Recognition
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Recently, a graph-based method was proposed for Linear Dimensionality Reduction (LDR). It is based on Locality Preserving Projections (LPP). It has been successfully applied in many practical problems such as face recognition. In order to solve the Small Size Problem that usually affects face recognition, LPP is preceded by a Principal Component Analysis (PCA). This paper has two main contributions. First, we propose a recognition scheme based on the concatenation of the features provided by PCA and LPP. We show that this concatenation can improve the recognition performance. Second, we propose a feasible approach to the problem of selecting the best features in this mapped space. We have tested our proposed framework on several public benchmark data sets. Experiments on ORL, UMIST, PF01, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition.