Accession Number:

ADA282552

Title:

Procrustes: A Feature Set Reduction Technique

Descriptive Note:

Final rept.

Corporate Author:

NAVAL UNDERSEA WARFARE CENTER DIV NEWPORT RI

Report Date:

1994-06-30

Pagination or Media Count:

47.0

Abstract:

This report explores the effectiveness of a new method for feature reduction and interpretation called Procrustes ordering. The investigation is performed using real data from eleven acoustic signal classes hold-out studies are used to establish confidence in the conclusions reached. A significance test of the Procrustes angles based on a feature generation model is proposed. Additionally, an experimental statistical methodology is introduced to evaluate varying feature orderings derived from multiple trials using the same data set. Procrustes ordering is used in conjunction with a new variation of Fishers method called smoothed Fisher. The variation is obtained by using a recently developed maximum likelihood trained probabilistic neural network, called Streits Probabilistic Neural Network SPNN, to provide smoothed estimates of the parameters defining the Fisher projection space. The results show that, on the given data set, Procrustes ordering used in conjunction with smoothed Fisher is an excellent method for feature reduction and interpretation. In addition, it is shown that Procrustes ordering is suitable for in situ application because it is fast and easy to compute on serial computers. Procrustes ordering, Probabilistic neural network

Subject Categories:

  • Statistics and Probability
  • Acoustics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE