Title :
Optimizing feature extraction for multiclass problems
Author :
Choi, Euisun ; Lee, Chulhee
Author_Institution :
Dept. of Electr. & Comput. Eng., Yonsei Univ., Seoul, South Korea
fDate :
3/1/2001 12:00:00 AM
Abstract :
Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, the authors propose a method to optimize feature extraction for multiclass problems. The authors first investigate the distribution of classification accuracies of multiclass problems in the feature space and find that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then the authors propose an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms
Keywords :
feature extraction; optimisation; remote sensing; algorithm; classification accuracies; feature extraction method; feature extraction optimization; multiclass problems; pattern classification; remotely sensed data; Autocorrelation; Covariance matrix; Eigenvalues and eigenfunctions; Feature extraction; Integral equations; Optimization methods; Pattern classification; Pattern recognition; Probability density function; Vectors;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on