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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.  ...  Compared with existing methods which are sensitive to noisy features, our proposed method utilizes a robust local learning method to construct the graph Laplacian and a robust spectral regression method  ...  This work is supported in part by China National 973 program 2014CB340301 and NSFC grant 61379043.  ... 
doi:10.1109/icdm.2014.58 dblp:conf/icdm/ShiDS14 fatcat:6nr5yqyranemjpkwjqlsy2ihoe

Sparse preserving feature weights learning

Guangsheng Xia, Hui Yan, Jian Yang
2016 Neurocomputing  
In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory, sparse  ...  Abstract In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory  ...  Wu, Feature selection via joint embedding learning and sparse regression, International Joint Conferences on Artificial Intelligence. Barcelona, Spain (2014) 793-804. [17] L. Shi, L. Du, Y. D.  ... 
doi:10.1016/j.neucom.2015.12.020 fatcat:evugk4mxpjhmrjw3q4rxwsyi5u

2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42

2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., and Nishino, K., Recognizing Material Properties from Images; 1981-1995 Sebe, N., see Pilzer, A., 2380-2395 Seddik, M., see Tamaazousti, Y., 2212-2224 Shah, M., see Kalayeh, M.M., TPAMI June 2020  ...  ., +, TPAMI April 2020 956-973 Unsupervised Video Matting via Sparse and Low-Rank Representation.  ...  ., +, TPAMI June 2020 1501-1514 Feature selection Logistic Regression Confined by Cardinality-Constrained Sample and Fea- ture Selection.  ... 
doi:10.1109/tpami.2020.3036557 fatcat:3j6s2l53x5eqxnlsptsgbjeebe

Feature Selection by Joint Graph Sparse Coding [chapter]

Xiaofeng Zhu, Xindong Wu, Wei Ding, Shichao Zhang
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
This paper takes manifold learning and regression simultaneously into account to perform unsupervised spectral feature selection.  ...  We first extract the bases of the data, and then represent the data sparsely using the extracted bases by proposing a novel joint graph sparse coding model, JGSC for short.  ...  Spectral feature selection can improve the performance of feature selection because it preserves the local structures of the data via manifold learning.  ... 
doi:10.1137/1.9781611972832.89 dblp:conf/sdm/0003W0Z13 fatcat:hedmcncvkjbn7c73c5wwjycejy

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  
To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously.  ...  The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data.  ...  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

Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph [article]

Shuchu Han, Hao Huang, Hong Qin
2017 arXiv   pre-print
Based on the sparse learning based unsupervised feature selection framework, Sparse Feature Graph (SFG) is introduced not only to model the redundancy between two features, but also to disclose the group  ...  With accurate data structure, quality indicator vectors can be obtained to improve the learning performance of existing unsupervised feature selection algorithms such as multi-cluster feature selection  ...  The sparse feature graph inherits the philosophy of sparse learning based unsupervised feature selection framework.  ... 
arXiv:1705.04804v2 fatcat:ctigqjona5ewhntxrqevm4edhe

Local and Global Discriminative Learning for Unsupervised Feature Selection

Liang Du, Zhiyong Shen, Xuan Li, Peng Zhou, Yi-Dong Shen
2013 2013 IEEE 13th International Conference on Data Mining  
In this paper, we propose the Local and Global Discriminative learning for unsupervised Feature Selection (LGDFS), which integrates a global and a set of locally linear regression model with weighted 2  ...  In this paper, we consider the problem of feature selection in unsupervised learning scenario.  ...  This indicates that the joint embedding learning and sparse regression framework is generally capable of enhancing both clustering and feature selection.  ... 
doi:10.1109/icdm.2013.23 dblp:conf/icdm/DuSLZS13 fatcat:l6uacdyfh5glph74y64b2ouas4

Unsupervised Feature Selection with Adaptive Structure Learning [article]

Liang Du, Yi-Dong Shen
2015 arXiv   pre-print
To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously.  ...  The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data.  ...  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

Dependence Guided Unsupervised Feature Selection

Jun Guo, Wenwu Zhu
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In the past decade, various sparse learning based unsupervised feature selection methods have been developed.  ...  To address this problem, we propose a Dependence Guided Unsupervised Feature Selection (DGUFS) method to select features and partition data in a joint manner.  ...  Selection (RUFS): Features are selected by joint l 2,1 -norm regularized regression and l 2,1 -norm based Nonnegative Matrix Factorization (NMF) with local learning (Qian and Zhai 2013) . • Embedded  ... 
doi:10.1609/aaai.v32i1.11904 fatcat:42hnbg6m7nb7jmqu6hnz5nblhu

Joint Clustering and Feature Selection [chapter]

Liang Du, Yi-Dong Shen
2013 Lecture Notes in Computer Science  
Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint Clustering and Feature Selection (JCFS).  ...  Due to the absence of class labels, unsupervised feature selection is much more difficult than supervised feature selection.  ...  , joint Feature Selection and Subspace Learning (FSSL) [8] and Joint Embedding Learning and Sparse Regression (JELSR) [9] .  ... 
doi:10.1007/978-3-642-38562-9_25 fatcat:q7xmswrd6rexjh7t2rfuilp3kq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 3845-3858 Unsupervised Feature Selection Via Data Reconstruction and Side Informa- tion.  ...  Fan, B., +, TIP 2020 8120-8133 Unsupervised Feature Selection Via Data Reconstruction and Side Informa- tion.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

Cen Wu, Fei Zhou, Jie Ren, Xiaoxi Li, Yu Jiang, Shuangge Ma
2019 High-Throughput  
Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively  ...  In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods.  ...  Ickstadt et al. [22] Bayesian Review integrative Bayesian methods for gene prioritization, subgroup identification via Bayesian clustering analysis, omics feature selection and network learning.  ... 
doi:10.3390/ht8010004 pmid:30669303 pmcid:PMC6473252 fatcat:6p5b3c7jlzb2bd56fky7pl3bxe

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.  ...  To better pre-process unlabeled data, most existing feature selection methods remove redundant and noisy information by exploring some intrinsic structures embedded in samples.  ...  Yanqing Guo from Dalian University of Technology (DUT), and Prof. Xiangwei Kong from Zhejiang University for reviewing an earlier version of this paper.  ... 
doi:10.1609/aaai.v34i01.5336 fatcat:3woh4jkl3fev3eunrivrgau3oq

Multi-task sparse screening for predicting future clinical scores using longitudinal cortical thickness measures

Jie Zhang, Yanshuai Tu, Qingyang Li, Richard J. Caselli, Paul M. Thompson, Jieping Ye, Yalin Wang
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
In this paper, we propose an unsupervised dictionary learning algorithm, termed Multi-task Sparse Screening (MSS) that produces improved results over previous methods within this problem domain.  ...  However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feature selection for AD diagnosis and prognosis  ...  Acknowledgments The research was supported in part by NIH (R21AG049216, RF1AG051710, R01EB025032 and U54EB020403) and NSF (DMS-1413417 and IIS-1421165).  ... 
doi:10.1109/isbi.2018.8363835 pmid:30023040 pmcid:PMC6047361 fatcat:wz5xcwlh5bfepfpzkt6tg3ts3e

Joint hypergraph learning and sparse regression for feature selection

Zhihong Zhang, Lu Bai, Yuanheng Liang, Edwin Hancock
2017 Pattern Recognition  
Here on the other hand, we perform data structure learning and feature * Corresponding author  ...  In this paper, we propose a uniÞed framework for improved structure estimation and feature selection.  ...  This paper introduces a novel feature selection framework: joint hypergraph learning and sparse regression (referred to as JHLSR).  ... 
doi:10.1016/j.patcog.2016.06.009 fatcat:2pdq5verzzclvik3tapvf43sf4
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