Feature Extraction Using an Unsupervised Neural Network

Abstract

A novel unsupervised neural network for dimensionality reduction which seeks directions emphasizing distinguishing features in the data is presented. A statistical framework for the parameter estimation problem associated with this neural network is given and its connection to exploratory projection pursuit methods is established. The network is shown to minimize a loss function (projection index) over a set of parameters, yielding an optimal decision rule under some norm. A specific projection index that favors directions possessing multimodality is presented. This leads to a similar form to the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features, is demonstrated using a linguistically motivated phoneme recognition experiment, and compared with feature extraction using principal components and back propagation network.

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Document Details

Document Type
Technical Report
Publication Date
May 03, 1991
Accession Number
ADA235581

Entities

People

  • Nathan Intrator

Organizations

  • Brown University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Applied Mathematics
  • Data Analysis
  • Differential Equations
  • Dimensionality Reduction
  • Equations
  • Feature Extraction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Military Research
  • Mixing
  • Neural Networks
  • Numbers
  • Recognition
  • Self Organizing Systems
  • Statistical Analysis

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks