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About Deep Neural Networks For High Dimension, Low Sample Size Data
This document is the abstract of a research paper about using deep neural networks (DNNs) for high-dimensional, low-sample-size data. The proposed method, called Deep Neural Pursuit (DNP), incrementally selects features using backpropagated gradients to build the DNN architecture and alleviate overfitting. It also calculates gradients with lower variance by averaging over multiple dropout runs. Experimental results on synthetic and biological datasets show DNP performs comparably or better than other feature selection methods for this type of data.
Detailed Information
| Author: | ['luizotaviocfg'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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