Investigation of Incremental Learning as Temporal Feature Extraction

Shoya Matsumori, Yuki Abe, Masahiko Osawa, Michita Imai

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


In this paper we discuss an effect of feature extraction using Incremental Learning Restricted Boltzmann Machine (IL-RBM). We trained the model on Moving MNIST and analyzed the obtained representation by visualizing hidden activities and reported some meaningful features obtained in incremental learning, similar to that of obtained in Slow Feature Analysis (SFA).

Original languageEnglish
Pages (from-to)342-347
Number of pages6
JournalProcedia Computer Science
Publication statusPublished - 2018
Event9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 - Prague, Czech Republic
Duration: 2018 Aug 222018 Aug 24


  • incremental learning restricted boltzmann machine
  • slow feature analysis
  • time series analysis

ASJC Scopus subject areas

  • General Computer Science


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