Factored four way conditional restricted Boltzmann machines for activity recognition

Decebal Constantin Mocanu*, Haitham Bou Ammar, Dietwig Lowet, Kurt Driessens, Antonio Liotta, Gerhard Weiss, Karl Tuyls

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


This paper introduces a new learning algorithm for human activity recognition capable of simultaneous regression and classification. Building upon conditional restricted Boltzmann machines (CRBMs), Factored four way conditional restricted Boltzmann machines (FFW-CRBMs) incorporate a new label layer and four-way interactions among the neurons from the different layers. The additional layer gives the classification nodes a similar strong multiplicative effect compared to the other layers, and avoids that the classification neurons are overwhelmed by the (much larger set of) other neurons. This makes FFW-CRBMs capable of performing activity recognition, prediction and self auto evaluation of classification within one unified framework. As a second contribution, sequential Markov chain contrastive divergence (SMcCD) is introduced. SMcCD modifies Contrastive Divergence to compensate for the extra complexity of FFW-CRBMs during training. Two sets of experiments one on benchmark datasets and one a robotic platform for smart companions show the effectiveness of FFW-CRBMs.
Original languageEnglish
Pages (from-to)100-108
Number of pages9
JournalPattern Recognition Letters
Publication statusPublished - 15 Nov 2015


  • Activity recognition
  • Deep learning
  • Restricted Boltzmann machines

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