Learning Invariant Features Using Subspace Restricted Boltzmann Machine

Jakub M. Tomczak*, Adam Gonczarek

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The subspace restricted Boltzmann machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace units. The subspace units reflect variations of a pattern in data and the gate unit is responsible for activating the subspace units. Additionally, the gate unit can be seen as a pooling feature. We evaluate the behavior of subspaceRBM through experiments with MNIST digit recognition task and Caltech 101 Silhouettes image corpora, measuring cross-entropy reconstruction error and classification error.

Original languageEnglish
Pages (from-to)173-182
Number of pages10
JournalNeural Processing Letters
Volume45
Issue number1
DOIs
Publication statusPublished - 1 Feb 2017
Externally publishedYes

Keywords

  • Deep model
  • Feature learning
  • Invariant features
  • Subspace features
  • Unsupervised learning

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