The Effect on Classification Error of Random Permutations of the Features in Representing Multivariate Data by Faces.
STANFORD UNIV CALIF DEPT OF STATISTICS
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A graphical method of representing multivariate data consists of having a computer draw a cartoon of a face which is determined by 18 parameters features including length of nose, curvature of mouth, slant of eyes, length of eyes, etc. If a sample of 8-dimensional vector observations is presented each component of a vector can be made to determine one of the 18 features and 10 constants can be selected for the remaining features. The resulting output is a series of faces, one for each 8 dimensional observation, which can be studied visually. An experiment was designed to evaluate the effect of a random permutation of the features on the visual ability to classify observations from two multivariate populations into two separate groups corresponding to the original populations. It is estimated that a random permutation may affect the error rate in this classification task by about 25. Author
- Statistics and Probability