@article{e817589551d548d2b64fae5acf0b06f2,
title = "Factored four way conditional restricted Boltzmann machines for activity recognition",
abstract = "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.",
author = "D.C. Mocanu and \{Bou Ammar\}, H. and D.J.C. Lowet and K. Driessens and A. Liotta and G. Weiss and K.P. Tuyls",
year = "2015",
doi = "10.1016/j.patrec.2015.01.013",
language = "English",
volume = "66",
pages = "100--108",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier B.V.",
}