We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated subspace through which we get our final representation via projection. This work follows from previous work done on Deep Canonical Correlation (DCCA), in which deep feed-forward neural networks were used to learn nonlinear transformations of data while maximizing correlation.
The traditional LSTM cell consists of four networks that are trained in parallel. These four networks are: the forget gate, the input gate, the cell state, and the output. Of particular note is the cell state, which forms a "constant error carousel." The cell state avoids the problem of vanishing gradients and forward activation exploding because it is not updated with a gradient and is controlled by the forget and input gates (which are @math bounded). @cite_2 .
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These Ecole polytechnique federale de Lausanne EPFL, n° 2366 (2001)Faculte informatique et communicationsJury: Paolo Frasconi, Roger Hersch, Martin Rajman, Jurgen Schmidhuber Public defense: 2001-4-6 Reference doi:10.5075 epfl-thesis-2366Print copy in library catalog Record created on 2005-03-16, modified on 2016-08-08