Semi-supervised Feature Selection via Spectral Analysis

By Zheng Zhao, Huan Liu. SDM, 2007

305 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f16ccd183bf032e893325b359d9d4cfebdaeeb5c

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. Yi Yang
2. Huan Liu
3. Jiliang Tang
4. Feiping Nie
5. Hanqing Lu
6. Shuicheng Yan
7. Zechao Li
8. J. Liu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Yi Yang
3. Shuicheng Yan
4. Luc Van Gool
5. Hanqing Lu
6. Feiping Nie
7. Andrea Vedaldi
8. Xuelong Li

Citations in the corpus

• Spectral feature selection for supervised and unsupervised learning (2007, background): “Another line of our future work is to study semi-supervise feature selection (Zhao & Liu, 2007) using this framework.”
• Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (2014, background): “Consequently, semi-supervised feature selection [9], [19] is developed to exploit both labeled and unlabeled data simultaneously.”
• Discriminative Least Squares Regression for Multiclass Classification and Feature Selection (2012, background): “Taking the class labels of data points into account or not, feature selection algorithms will fall into three categories: unsupervised algorithms [9], semi-supervised algorithms [10]–[12], and supervised algorithms [7], [13].”
• A survey on feature selection methods (2014, methodology): “In [67] the authors use a clustering indicator construction to score a set of features.”
• Trace Ratio Criterion for Feature Selection (2008, methodology): “Instead of calculating the scores of all the feature subsets, traditional methods calculate the score for each feature, and then select the leading features based on the rank of these feature-level scores.”
• Feature Selection for Classification: A Review (2014, background): “concept, semi-supervised feature selection makes use of both labeled and unlabeled data to estimate feature relevance [77].”
• 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].”
• Feature Selection for Clustering: A Review (2018, background): “According to whether the label information is utilized, different feature selection algorithms can be categorized into supervised [69, 60], unsupervised [18, 46], or semi-supervised algorithms [79, 73].”
• Partially supervised feature selection with regularized linear models (2009, background): “Semi-supervised classification deals with problems for which only a fraction of the learning examples have known class labels, and semi-supervised feature selection methods have been recently proposed (Zhao & Liu, 2007; Cheng et al., 2008).”