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.
- Activity recognition
- Deep learning
- Restricted Boltzmann machines
Mocanu, D. C., Bou Ammar, H., Lowet, D., Driessens, K., Liotta, A., Weiss, G., & Tuyls, K. (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