Optimal feature extraction for normally distributed data

Chulhee Lee, Euisun Choi, Jaehong Kim

Research output: Contribution to journalConference articlepeer-review


In this paper, we propose an optimal feature extraction method for normally distributed data. The feature extraction algorithm is optimal in the sense that we search the whole feature space to find a set of features which give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly in the direction so that the classification error decreases most rapidly. This can be done by taking gradient. We propose two search methods, sequential search and global search. In the sequential search, if more features are needed, we try to find an additional feature which gives the best classification accuracy with the already chosen features. In the global search, we are not restricted to use the already chosen features. Experiment results show that the proposed method outperforms the conventional feature extraction algorithms.

Original languageEnglish
Pages (from-to)223-232
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1998
EventAlgorithms for Multispectral and Hyperspectral Imagery IV - Orlando, FL, United States
Duration: 1998 Apr 131998 Apr 14

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optimal feature extraction for normally distributed data'. Together they form a unique fingerprint.

Cite this