Recurrent clustering for unsupervised feature extraction with application to sequence detection

Steven Robert Young, Itamar Arel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In many unsupervised learning applications both spatial and temporal regularities in the data need to be represented. Traditional clustering algorithms, which are commonly employed by unsupervised learning engines, lack the ability to naturally capture temporal dependencies. In supervised learning methods, temporal features are often learned through the use of a feedback (or recurrent) signal. Drawing inspiration from the Elman recurrent neural network, we introduce a winner-take-all based recurrent clustering algorithm that is able to identify temporal regularities in an unsupervised manner. We explore the potential pitfalls that result from adding feedback to an incremental clustering algorithm and apply the proposed technique to several time series inference problems in the context of semi-supervised learning. The results clearly indicate that the framework can be broadly applied with particular relevance to scalable deep machine learning architectures.

Original languageEnglish
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages54-55
Number of pages2
DOIs
StatePublished - 2012
Externally publishedYes
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume2

Conference

Conference11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Country/TerritoryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • recurrent clustering
  • semi-supervised learning
  • spatiotemporal features
  • time series analysis

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