| Autor: |
Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund, Saikat Chatterjee |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-19 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1687-6180 |
| DOI: |
10.1186/s13634-020-00695-2 |
| Popis: |
Abstract We design a rectified linear unit-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure a reduction of the training cost as the number of layers increases. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An ℓ 2-norm convex constraint is used in the minimization to reduce the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost, and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer. We show that the proposed architecture is norm-preserving and provides an invertible feature vector and, therefore, can be used to reduce the training cost of any other learning method which employs linear projection to estimate the target. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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