Spectral feature selection for supervised and unsupervised learning

By Zheng Zhao, Huan Liu. International Conference on Machine Learning, 2007

983 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/822b444a9c6920ee5e7952a50121990715d4ddd1

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. Yi Yang
4. Xuelong Li
5. Feiping Nie
6. Deng Cai
7. Jundong Li
8. Hanqing Lu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Yi Yang
3. Xuelong Li
4. Shuicheng Yan
5. Luc Van Gool
6. Feiping Nie
7. Xiaogang Wang
8. Trevor Darrell

Citations in the corpus

• Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (2014, methodology): “The filter algorithms [7], [20]–[22] use a proxy measure instead of the error rate to score a feature subset.”
• Robust Structured Subspace Learning for Data Representation (2015, background): “According to the availability of label information, there are three broad categories: supervised [4], [13], [15], semi-supervised [16] and unsupervised feature selection methods [5], [17], [18].”
• Unsupervised feature selection for multi-cluster data (2010, methodology): “Recently, the LaplacianScore algorithm [17] and its extensions [30] have been proposed to select those features which can best reflect the underlying manifold structure.”
• Unsupervised Feature Selection Using Nonnegative Spectral Analysis (2012, methodology): “The filter methods (Duda, Hart, and Stork 2001; He, Cai, and Niyogi 2005; Zhao and Liu 2007; Masaeli, Fung, and Dy 2010; Liu, Wu, and Zhang 2011; Yang et al. 2011a) use statistical properties of the features to filter out poorly informative ones.”
• Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (2009, background): “A number of performance criteria have been proposed for feature selection, including mutual information [19], [31], maximum margin [22], [52], kernel alignment [15], [37], graph-spectrum based measures [53], [66], construction errors in neural network [35], [42], worst case probability [4], [61], an”
• A review of unsupervised feature selection methods (2019, methodology): “Spectral/sparse learningmethods Some multivariate methods based on Spectral Analysis derived from the SPECand theLaplacian Scorewere introduced inGarcia-Garcia and SantosRodriguez (2009), Liu et al. (2009b), Niijima and Okuno (2009).”
• On Similarity Preserving Feature Selection (2013, methodology): “In our recent study, we observe that a number of existing feature selection algorithms are essentially based on assessing features’ capability in preserving sample similarity, which can be inferred from either label information or predefined distance metric [49].”
• An improved approximation algorithm for the column subset selection problem (2008, result): “We should note that similar formulations appeared in [23, 33, 36, 37, 26, 1].”
• Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, methodology): “LapScor and SPEC then compute different metrics to rank each feature.”
• Efficient Spectral Feature Selection with Minimum Redundancy (2010, methodology): “2008), relief and reliefF (Sikonja and Kononenko 2003), SPEC (Zhao and Liu 2007), and HSIC (Song et al.”