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Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering [article]

Mohsen Ghassemi Parsa, Hadi Zare, Mehdi Ghatee
2019 arXiv   pre-print
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms.  ...  In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces  ...  ] , global and local similarity preserving feature selection, GLSPFS [30] , and unsupervised feature selection with adaptive structure learning, FSASL [31] .  ... 
arXiv:1912.05458v1 fatcat:ncvvwekf7fgbljhhpgvn7n5oem

Unsupervised Feature Selection with Adaptive Structure Learning

Liang Du, Yi-Dong Shen
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data.  ...  To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously.  ...  Feiping Nie and Prof. Mingyu Fan for their helpful suggestions to improve this paper.  ... 
doi:10.1145/2783258.2783345 dblp:conf/kdd/DuS15 fatcat:lxg6miunrncgzmp4by352dklvy

Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection [article]

Zhenzhen Sun, Yuanlong Yu
2022 arXiv   pre-print
To tackle these problems, we propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method in this paper, in which a l_2,0-norm regularization term  ...  with respect to transformation matrix is imposed in the manifold learning for feature selection, and a graph regularization term is incorporated into the learning model to learn the local geometric structure  ...  Unsupervised discriminative feature selection (UDFS) (Yang et al., 2011) incorporates discriminative analysis and l 2,1 -norm minimization into a joint framework for unsupervised feature selection.  ... 
arXiv:2010.05454v3 fatcat:dvixcbjq25ahvovzmftwwgygnu

Unsupervised Person Re-identification via Multi-order Cross-view Graph Adversarial Network

Xiang Fu, Xinyu Lai
2021 IEEE Access  
Unsupervised person re-identification (re-id) is an effective analysis for video surveillance in practice, which can train a pedestrian matching model without any annotations, and it is easy to deploy  ...  a view-shared feature space, which is iteratively trained by a graph generative adversarial learning strategy to deeply bridge the distribution-gap.  ...  network, unsupervised graph adversarial learning and multi-order discriminative feature learning.  ... 
doi:10.1109/access.2020.3048834 fatcat:v3bhppj4qnccjmsmmrzfgbwie4

Unsupervised Feature Selection with Adaptive Structure Learning [article]

Liang Du, Yi-Dong Shen
2015 arXiv   pre-print
The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data.  ...  To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously.  ...  The typical methods include: joint embedding learning and spectral regression (JELSR) [12] , [11] , nonnegative discriminative feature selection (NDFS) [15] , robust unsupervised feature selection (  ... 
arXiv:1504.00736v1 fatcat:w6int3ap6zd5plmubmdz4bihuu

Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing

Yun Fu, Zhu Li, Junsong Yuan, Ying Wu, T.S. Huang
2008 IEEE transactions on circuits and systems for video technology (Print)  
Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification.  ...  We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured  ...  Joint Local Models The more effective way for designing discriminative learning algorithms is to integrate different local concepts.  ... 
doi:10.1109/tcsvt.2008.2004933 fatcat:szdl5lkhbfbx7ihwhf6ryuwsya

Disjoint Label Space Transfer Learning with Common Factorised Space

Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
It is shared between source and target domains, and trained with an unsupervised factorisation loss and a graph-based loss.  ...  as unsupervised domain adaptation, where the source and target domains share the same label-sets.  ...  And to improve feature learning for subsequent tasks such as retrieval, a novel graph-based loss is further proposed.  ... 
doi:10.1609/aaai.v33i01.33013288 fatcat:jjnhyseegzginhjhkqsrpykohe

Disjoint Label Space Transfer Learning with Common Factorised Space [article]

Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales
2018 arXiv   pre-print
It is shared between source and target domains, and trained with an unsupervised factorisation loss and a graph-based loss.  ...  as unsupervised domain adaptation, where the source and target domains share the same label-sets.  ...  And to improve feature learning for subsequent tasks such as retrieval, a novel graph-based loss is further proposed.  ... 
arXiv:1812.02605v1 fatcat:o26mlpwkbfcxbh5nirdy3fhnt4

Unsupervised Feature Selection for Multi-View Clustering on Text-Image Web News Data

Mingjie Qian, Chengxiang Zhai
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
robust joint l2,1-norm minimization is performed to select discriminative features.  ...  State-of-theart multi-view unsupervised feature selection methods learn pseudo class labels by spectral analysis, which is sensitive to the choice of similarity metric for each view.  ...  Parameter Analysis We plot ACC versus different α, β, and number of selected features on FOXNews for MVUFS in Figure 2 (similar figures for NMI and on CNN dataset) due to space limit.  ... 
doi:10.1145/2661829.2661993 dblp:conf/cikm/QianZ14 fatcat:zva5hdsur5hkfiajtip3r4npzm

Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data

Jun Guo, Heng Chang, Wenwu Zhu
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
This paper proposes an unsupervised triplet-induced graph to explore a new type of potential structure at feature level, and incorporates it into simultaneous feature selection and clustering.  ...  In the feature selection part, we design an ordinal consensus preserving term based on a triplet-induced graph.  ...  We would like to thank the anonymous reviewers for their helpful comments. We also thank Prof. Yi Ma from UC Berkeley, Prof. Shengyu Zhang and Dr.  ... 
doi:10.1609/aaai.v34i01.5336 fatcat:3woh4jkl3fev3eunrivrgau3oq

Robust Spectral Learning for Unsupervised Feature Selection

Lei Shi, Liang Du, Yi-Dong Shen
2014 2014 IEEE International Conference on Data Mining  
In this paper, we propose a Robust Spectral learning framework for unsupervised Feature Selection (RSFS), which jointly improves the robustness of graph embedding and sparse spectral regression.  ...  In this paper, we consider the problem of unsupervised feature selection.  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.1109/icdm.2014.58 dblp:conf/icdm/ShiDS14 fatcat:6nr5yqyranemjpkwjqlsy2ihoe

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 782-795 Discriminative and Uncorrelated Feature Selection With Constrained Spec- tral Analysis in Unsupervised Learning.  ...  ., +, TIP 2020 9572-9583 Discriminative and Uncorrelated Feature Selection With Constrained Spec- tral Analysis in Unsupervised Learning.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Image analysis with nonlinear adaptive dimension reduction

Xinlei Chen, Xinquan Qu, Zijian Li
2011 Proceedings of the Third International Conference on Internet Multimedia Computing and Service - ICIMCS '11  
Moreover, the incorporation of K-means enables NADR to be a powerful alternative for cluster analysis.  ...  Recently, its adaptive variants have received considerable attention in unsupervised learning since a single pass without label information often fails to guarantee an optimal representation, especially  ...  And actually many of them are specially designed for a more discriminative unsupervised learning method.  ... 
doi:10.1145/2043674.2043713 dblp:conf/icimcs/ChenQL11 fatcat:2yjlrwprtzeq3oeevijs4qf3la

Adaptive Unsupervised Feature Learning for Gene Signature Identification in Non-small-cell Lung Cancer

Xiucai Ye, Weihang Zhang, Tetsuya Sakurai
2020 IEEE Access  
The proposed method incorporated linear discriminant analysis, adaptive structure preservation, and l 2,1 -norm sparse regression into a joint learning framework for unsupervised feature selection to select  ...  Unsupervised feature selection is an effective computational technique for searching the most discriminative feature subset to distinguish different classes and find the potential information embedded  ...  The proposed method incorporated linear discriminant analysis, adaptive structure preservation, and l 2,1 -norm sparse regression into a joint learning framework for unsupervised feature learning to select  ... 
doi:10.1109/access.2020.3018480 fatcat:3mu6bwxiobhdfo3gtxx6xyv65i

Unsupervised Feature Selection via Multi-step Markov Transition Probability [article]

Yan Min, Mao Ye, Liang Tian, Yulin Jian, Ce Zhu, Shangming Yang
2020 arXiv   pre-print
Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov transition probability for Feature Selection).  ...  Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability.  ...  nonnegative matrix factorization and l 2,1 -norm minimization.7) Adaptive Unsupervised Feature Selection (AUFS) [56]: It uses a joint adaptive loss for data fitting and a l 2,0 minimization for feature  ... 
arXiv:2005.14359v1 fatcat:5rs2v2d2rzhqnjvk4auluzwncu
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