Methods of Sparse Modeling and Dimensionality Reduction to Deal with Big Data

Abstract

This project focused on the development of new methods for sparse modeling and non-negative matrix factorization. The major achievements are 1) a sparse topic model that can learn thousands of topics from a large set of documents and infer the topic mixture of each document, 2) a supervised dimension reduction method for large datasets, and 3) a non-negative matrix factorization (NMF) method with good interpretability. The research on sparse topic model and supervised dimension reduction as well as NMF is motivated by the need of reducing complexity in dealing with huge and complex datasets in big data. The proposed methods were theoretically and experimentally evaluated, and applied to problems in materials science and biomedicine.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2015
Accession Number
ADA623178

Entities

People

  • Tu Bao Ho

Organizations

  • Japan Advanced Institute of Science and Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Big Data
  • Computational Science
  • Computer Programs
  • Data Analysis
  • Data Mining
  • Dimensionality Reduction
  • Engineering
  • Information Science
  • Machine Learning
  • Materials
  • Materials Science
  • Network Science
  • Probabilistic Models

Fields of Study

  • Computer science

Readers

  • Academic Conference Management
  • Linear Algebra
  • Neural Network Machine Learning.

Technology Areas

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