Supervised Learning via Unsupervised Sparse Autoencoder

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly...

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Vydané v:	IEEE access Ročník 6; s. 73802 - 73814
Hlavní autori:	Liu, Jianran, Li, Chan, Yang, Wenyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Classification Cognitive tasks Data mining Datasets Dimensionality reduction Feature extraction feature representation Labels Machine learning Optimization Parameter sensitivity Principal component analysis Reduction Representations sparse autoencoder Stress Supervised learning
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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