Learning Latent Variable and Predictive Models of Dynamical Systems

Abstract

In this thesis we propose novel learning algorithms that address the issues of model selection, local minima and instability in learning latent variable models. We show that certain 'predictive' latent variable model learning methods bridge the gap between latent variable and predictive models. We also propose a novel latent variable model, the Reduced-Rank HMM (RR-HMM), that combines desirable properties of discrete and real-valued latent-variable models. We show that reparameterizing the class of RR-HMMs yields a subset of PSRs, and propose an asymptotically unbiased predictive learning algorithm for RR-HMMs and PSRs along with finite-sample error bounds for the RR-HMM case. In terms of efficiency and accuracy, our methods outperform alternatives on dynamic texture videos, mobile robot visual sensing data, and other domains.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2009
Accession Number
ADA515929

Entities

People

  • Sajid M. Siddiqi

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Data Mining
  • Hidden Markov Models
  • Information Science
  • Machine Learning
  • Markov Models
  • Mathematical Filters
  • Network Science
  • Predictive Modeling
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistics
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy