A Kernel Machine Framework for Feature Optimization in Multi-frequency Sonar Imagery

Abstract

The purpose of this research is to optimize the extraction of classification features. This includes the optimal adjustment of parameters used to compute features as well as an objective and quantitative method to assist in choosing a priori data collection parameters (e.g., the insonification frequencies of a multi-frequency sonar). To accomplish this, a kernel machine is employed and implemented with the kernel matching pursuits (KMP) algorithm. The KMP algorithm is computationally efficient, allows the use of arbitrary kernel mappings, and facilitates the development of a technique to quantify discriminating power as a function of each feature. A method for feature optimization is then presented and evaluated on simulated and experimental data. The experimental data is derived from low-resolution, multi-frequency sonar and consists of a large feature space relative to the available training data. The proposed method successfully optimizes the feature extraction parameters and identifies the (much smaller) subset of features actually providing the discriminating capability.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA498069

Entities

People

  • J. R. Stack
  • L. Carin
  • Roy Arrieta
  • X. Liao

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Classification
  • Data Acquisition
  • Data Sets
  • Dimensionality Reduction
  • Extraction
  • Feature Extraction
  • Frequency
  • Information Science
  • Learning Machines
  • Low Resolution
  • Machine Learning
  • Neural Networks
  • Optimization
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Acoustical Oceanography.
  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space