Article ID Journal Published Year Pages File Type
6874154 Information Processing Letters 2018 14 Pages PDF
Abstract
In this work, we exploit a novel algorithm for capturing the Lie group manifold structure of the visual impression. By developing the single-layer Lie group model, we show how the representation learning algorithm can be stacked to yield a deep architecture. In addition, we design a Lie group based gradient descent algorithm to solve the learning problem of network weights. We show that our proposed technique yields representations that significantly better suited for training deep network and is also computationally efficient.
Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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