SPIDER: Subspace Primitives that are Interpretable and Diverse

Abstract

Contribute three classes of primitives to the D3M program that are not only innovative machine learning methods but also provide a certain transparent mechanism by which the domain expert, data science-novice can naturally incorporate their expert knowledge to yield a better model. The three classes of primitives each attack the discovery of subspaces within the input data space with different tools: (1) multimodal embeddings leverage sparse models, (2) invariance discovery primitives, and (3) subspace clustering primitives.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2021
Accession Number
AD1130271

Entities

People

  • Jason J. Corso
  • Laura Balzano

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Data Mining
  • Data Science
  • Databases
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Kalman Filters
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Pattern Recognition
  • Processing Equipment
  • Signal Processing

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Space