Nguyen, Tu Dinh, Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2015, Tensor-variate restricted Boltzmann machines, in AAAI 2015: The Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI Press, Palo Alto, Calif., pp. 2887-2893.
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance.
Notes
paper No: 2887
ISBN
9781577357018
Language
eng
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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