Statistical Analysis and Classification of Acoustic Color Functions

Abstract

In this paper we present a method for clustering and classification of acoustic color data based on statistical analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms the Fisher-Rao metric into the standard L2 metric. As a result, a formal distance can be calculated using geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models can then be constructed using principal component analysis to model the functional variation of acoustic color data. Empirical results demonstrate the utility of functional data analysis for improving performance results in pattern recognition using acoustic color data.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2011
Accession Number
ADA544774

Entities

People

  • Anuj Srivastava
  • J. D. Tucker

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Detection
  • Detectors
  • Factor Analysis
  • Frequency
  • Frequency Bands
  • Identification
  • Information Science
  • Numbers
  • Pattern Recognition
  • Statistical Analysis
  • Statistics
  • Two Dimensional

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.
  • Regression Analysis.

Technology Areas

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