Quantifying Similarity and Distance Measures for Vector-Based Datasets: Histograms, Signals, and Probability Distribution Functions

Abstract

It is often important to characterize the similarity or dissimilarity (distance) between different measured or computed datasets. There are a large number of different possible similarity and distance measures that can be applied to different datasets. In this technical note, a number of different measures implemented in both MATLAB and Python as functions are used to quantify similarity/distance between 2 vector-based datasets. The scripts are attached as appendixes as is a description of their execution.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2017
Accession Number
AD1026967

Entities

People

  • Efran Hernandez-rivera
  • Mark Tschopp

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Diffraction
  • Distribution Functions
  • Histograms
  • Intellectual Property
  • Materials
  • Materials Science
  • Military Research
  • Operating Systems
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Reliability
  • Two Dimensional
  • X Rays
  • X-Ray Diffraction

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.
  • Regression Analysis.