Discriminative Semi-Supervised Feature Selection Via Manifold Regularization

By Zenglin Xu, Rong Jin, Michael R. Lyu, Irwin King. IEEE Transactions on Neural Networks, 2009

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Rheumatology

View Paper: https://www.semanticscholar.org/paper/6dc778f828e3e8364218f7b684afda1fc06e4f9d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Liu
2. Jiliang Tang
3. Feiping Nie
4. Ferat Sahin
5. Girish Chandrashekar
6. Irwin King
7. Michael R. Lyu
8. Lei Wang

In Computer Vision and Pattern Recognition

1. Feiping Nie
2. Huan Liu
3. Jiliang Tang
4. Dacheng Tao
5. Ferat Sahin
6. Girish Chandrashekar
7. Lei Wang
8. Changshui Zhang

In Rheumatology

1. Ferat Sahin
2. Girish Chandrashekar
3. Changshui Zhang
4. Jieping Ye

Citations in the corpus

• Feature Selection for Classification: A Review (2014, background): “According to whether the training set is labelled or not, feature selection algorithms can be categorized into supervised [68, 61], unsupervised [13, 51] and semi-supervised feature selection [77, 71].”
• On Similarity Preserving Feature Selection (2013, methodology): “According to the way of utilizing label information, feature selection algorithms can be categorized as supervised algorithms [36], [42], unsupervised algorithms [11], [19] or semi-supervised algorithms [44], [50].”
• Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, background): “It always focuses on the general characteristics of the data [16].”
• Simple and Efficient Multiple Kernel Learning by Group Lasso (2010, methodology): “We compare MKLGL with several state-of-the-art efficient MKL algorithms including: Semi-infinite Programming (SIP) (Kloft et al., 2009; Sonnenburg et al., 2006), the subgradient descent approach used in the SimpleMKL toolbox (Rakotomamonjy et al., 2008), and the level method (Xu et al., 2009a).”
• A review of feature selection methods with applications (2015, background): “An interested reader is referred to [16] for more information.”
• Discriminative Least Squares Regression for Multiclass Classification and Feature Selection (2012, background): “In addition, multiple criteria have been proposed, including maximum mutual information [14]–[16], maximum margin [11], minimum reconstruction errors [17], [18], low misclassification error in neural network [19], probabilistic prediction [20], sparse representation [21]–[28], stability [29],…”
• Feature selection in machine learning: A new perspective (2018, methodology): “N U According to the theoretical principle, feature selection methods can be based on statistics [35-39], information theory [40-45], manifold [46-48], and rough set [49-53], and can be categorized according to various standards. a) According to the utilized training data (labeled, unlabeled, or…”
• A survey on feature selection methods (2014, methodology): “In feature ranking, important features that are less informative on their own but are informative when combined with others could be discarded [1,29].”