Sparse Denoising Autoencoder Application in Identification of Counterfeit Pharmaceutical
Near-infrared(NIR)As a fast and non-destructive testing technology, spectroscopy techniques is very suitable for pharmaceutical discrimination. Autoencoder network, as a hot research topic, has drawn widespread attention in machine learning research in recent years. Compared with traditional surface...
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| Veröffentlicht in: | Guang pu xue yu guang pu fen xi Jg. 36; H. 9; S. 2774 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Chinesisch |
| Veröffentlicht: |
China
01.09.2016
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| ISSN: | 1000-0593 |
| Online-Zugang: | Weitere Angaben |
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| Zusammenfassung: | Near-infrared(NIR)As a fast and non-destructive testing technology, spectroscopy techniques is very suitable for pharmaceutical discrimination. Autoencoder network, as a hot research topic, has drawn widespread attention in machine learning research in recent years. Compared with traditional surface learning algorithm models, Autoencoder network has more powerful modeling capability as a typical deep networks model. Based on the unsupervised greedy layer-wise pre-training, autoencoder trains the network layer by layer while minimizing the error in reconstructing. Each layer is pre-trained with an unsupervised learning algorithm, learning a nonlinear transformation of the input of each layer which is the output of the previous layer. Pre-whitening process could get the inner structural features of the data more effectively. The supervised fine-tuning is followed with the unsupervised pre-training which sets the stage for a final training phase. The deep architecture is fine-tuned with respect to a supervised t |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1000-0593 |
| DOI: | 10.3964/j.issn.1000-0593(2016)09-2774-06 |