A Comprehensive Approach To Outlier Detection and Event Classification.

Abstract

In this report, a comprehensive approach to outlier detection and event classification is investigated. Although the methodology is based on the assumption of available training data, it does not require ground truth or labels of any type. In fact, it is not even required that the number of different populations composing the training data is known. Data from Western China is analyzed to demonstrate the methodology, as well as some simulated data. These examples demonstrate vividly the importance of the role of correlation in selecting the best features. A method for feature selection is considered. Additionally, the problems of classifying events, numerical stability, missing data, signal to noise ratios, and mixture (discrete and continuous) data are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1996
Accession Number
ADA324277

Entities

People

  • H. L. Gray
  • S. R. Sain
  • W. A. Woodward

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Atmospheric Sciences
  • Change Detection
  • Classification
  • Data Science
  • Detection
  • Earth Sciences
  • Feature Selection
  • Frequency Bands
  • Geography
  • Geophysics
  • Information Science
  • Oceanography
  • Statistical Analysis
  • Two Dimensional
  • Universities

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • Speech Processing/Speech Recognition.