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
Journal of Cognitive Neuroscience
Top 10 algorithms in data mining
Knowledge and Information Systems
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Past work on Linear Dimensionality Reduction (LDR)has emphasized the issues of classification and dimension estimation. However, relatively less attention has been given to the critical issue of eigenvector selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA) without using an effective scheme to select an appropriate set of features/eigenvectors in this space. This paper addresses Linear Dimensionality Reduction through Eigenvector selection for object recognition. It has two main contributions. First, we propose a unified framework for one transform based LDR. Second, we propose a framework for two transform based DLR. As a case study, we consider PCA and Linear Discriminant Analysis (LDA) for the linear transforms. We have tested our proposed frameworks on several public benchmark data sets. Experiments on ORL, UMIST, and YALE Face Databases and MNIST Handwritten Digit Database show significant performance improvements in recognition that are based on eigenvector selection.