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...
Main Authors: | Jianran Liu, Chan Li, Wenyuan Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8558569/ |
Similar Items
-
A General Approach for Achieving Supervised Subspace Learning in Sparse Representation
by: Jianshun Sang, et al.
Published: (2019-01-01) -
Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine
by: Xiaoping Fang, et al.
Published: (2020-02-01) -
A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
by: Xiaoxia Liang, et al.
Published: (2020-09-01) -
Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders
by: Lloyd Windrim, et al.
Published: (2019-04-01) -
Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
by: Fen Cai, et al.
Published: (2019-09-01)