The Effect on Classification Error of Random Permutations of the Features in Representing Multivariate Data by Faces.
Abstract
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)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 14, 1973
- Accession Number
- AD0775244
Entities
People
- Herman Chernoff
- M. Haseeb Rizvi
Organizations
- Stanford University