R-ELMNet: regularized extreme learning machine network
Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the stru...
Main Authors: | Zhang, Guanghao, Li, Yue, Cui, Dongshun, Mao, Shangbo, Huang, Guang-Bin |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160941 |
Similar Items
-
Unsupervised feature learning with sparse Bayesian auto-encoding based extreme learning machine
by: Zhang, Guanghao, et al.
Published: (2022) -
MMPCANet: An Improved PCANet for Occluded Face Recognition
by: Zewei Wang, et al.
Published: (2022-03-01) -
Micro-Expression Recognition Based on Optical Flow and PCANet+
by: Shiqi Wang, et al.
Published: (2022-06-01) -
Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis
by: Fahman Saeed, et al.
Published: (2023-01-01) -
Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet
by: Nan Wang, et al.
Published: (2018-12-01)