Applied multivariate statistical analysis
Applied multivariate statistical analysis
An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Multiple Comparisons in Induction Algorithms
Machine Learning
Searching for Features Defined by Hyperplanes
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Independent Component Analysis of Face Images
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Applications of Neural Blind Separation to Signal and Image Processing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
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In research of pattern recognition, we always want to achieve the correct classification rate according to the characteristics required. Feature extraction greatly affects the design and performance of the classifier, and it is one of the core issue of PR research. As an important component of pattern recognition, feature extraction has been paid close attention by many scholars, and currently has become one of the research hot spots in the field of pattern recognition. This article gives a general discussion of feature extraction, includes linear feature extraction and nonlinear feature extraction, and introduces the frontier methods of this field, at last discusses the development tendency of feature extraction.