Block-Row Sparse Multiview Multilabel Learning for Image Classification

IEEE Transactions on Cybernetics
Xiaofeng ZhuShichao Zhang

Abstract

In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an ℓ2,1-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the ℓ2,1-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer)...Continue Reading

References

Oct 4, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yue GaoTat-Seng Chua
May 23, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yue GaoQionghai Dai
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Jun 26, 2013·IEEE Transactions on Cybernetics·Richang HongXindong Wu
Jul 13, 2013·IEEE Transactions on Cybernetics·Dapeng TaoXuelong Li
Dec 19, 2013·IEEE Transactions on Cybernetics·Xiaoqiang Lu, Xuelong Li
Aug 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Sabri Boutemedjet, Djemel Ziou
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Kaibing ZhangXuelong Li
Jun 27, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Xiaofeng ZhuZi Huang
Oct 28, 2014·IEEE Transactions on Cybernetics·Kaihua ZhangXuelong Li
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Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kaihua ZhangMing-Hsuan Yang

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Citations

Aug 16, 2015·IEEE Transactions on Bio-medical Engineering·Xiaofeng ZhuDinggang Shen
Mar 16, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Ruxin Wang, Dacheng Tao
Jul 9, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Jiuwen CaoXiaoping Lai
Mar 10, 2016·IEEE Transactions on Neural Networks and Learning Systems·Xiaofeng ZhuXindong Wu
Feb 18, 2017·IEEE Transactions on Cybernetics·Jun HuangXindong Wu
May 24, 2017·IEEE Journal of Biomedical and Health Informatics·Zhen YuTianfu Wang
May 5, 2018·IEEE Transactions on Big Data·Xiaofeng ZhuDinggang Shen
Aug 17, 2016·IEEE Transactions on Cybernetics·Jun YuDacheng Tao
Oct 15, 2019·World Wide Web·Xiaofeng ZhuDinggang Shen
May 20, 2021·Computational Intelligence and Neuroscience·Yusra Khalid BhattiSergio A Velastin

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