Manifold Matching: Joint Optimization of Fidelity and Commensurability

Abstract

Fusion and inference from multiple and massive disparate data sources the requirement for our most challenging data analysis problems and the goal of our most ambitious statistical pattern recognition methodologies has many and varied aspects which are currently the target of intense research and development. One aspect of the overall challenge is manifold matching identifying embeddings of multiple disparate data spaces into the same low-dimensional space where joint inference can be pursued. We investigate this manifold matching task from the perspective of jointly optimizing the fidelity of the embeddings and their commensurability with one another, with a specific statistical inference exploitation task in mind. Our results demonstrate when and why our joint optimization methodology is superior to either version of separate optimization. The methodology is illustrated with simulations and an application in document matching.

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Document Details

Document Type
Technical Report
Publication Date
Nov 12, 2011
Accession Number
ADA565568

Entities

People

  • Carey E. Priebe
  • David J. Marchette
  • Sancar Adali
  • Zhiliang Ma

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algebraic Geometry
  • Applied Mathematics
  • Coordinate Systems
  • Correlation Analysis
  • Dimensionality Reduction
  • Geometry
  • Information Science
  • Language
  • Natural Language Processing
  • Pattern Recognition
  • Probability
  • Random Variables
  • Standards
  • Statistical Inference
  • Statistics
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

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