Factored four way conditional restricted Boltzmann machines for activity recognition

D.C. Mocanu, H. Bou Ammar, D.J.C. Lowet, K. Driessens, A. Liotta, G. Weiss, K.P. Tuyls

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

25 Citaten (Scopus)
126 Downloads (Pure)

Samenvatting

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.
Originele taal-2Engels
Pagina's (van-tot)100-108
TijdschriftPattern Recognition Letters
Volume66
DOI's
StatusGepubliceerd - 2015

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  • Citeer dit

    Mocanu, D. C., Bou Ammar, H., Lowet, D. J. C., Driessens, K., Liotta, A., Weiss, G., & Tuyls, K. P. (2015). Factored four way conditional restricted Boltzmann machines for activity recognition. Pattern Recognition Letters, 66, 100-108. https://doi.org/10.1016/j.patrec.2015.01.013