Laplacian Score for Feature Selection

By Xiaofei He, Deng Cai, Partha Niyogi. Neural Information Processing Systems, 2005

2,300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f7060dcaeda81c52db2267838c4c80b470ca780e

How researchers see this paper

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

1. Dacheng Tao
2. Feiping Nie
3. Yi Yang
4. Jundong Li
5. Zechao Li
6. Jiliang Tang
7. Huan Liu
8. Deng Cai

Citations in the corpus

• Partially Supervised Graph Embedding for Positive Unlabelled Feature Selection (2016, methodology): “Representative unsupervised feature selection methods include Laplacian Score [He 2005], Multi-Cluster Feature Selection (MCFS) [Cai 2010], Feature Selection via Joint Embedding Learning and Sparse Regression (JELSR)[Chenping Hou and Wu, 2011], Unsupervised Discriminative Feature Selection (UDFS) [Y”
• Kernel Methods and Machine Learning (2014, methodology): “Laplacian linear discriminant analysis-based recursive feature elimination (LLDARFE), which was proposed by Niijima and Okuno [196], is a multivariate feature selection technique that is closely related to the Laplacian score [96] and the Q–α algorithm [302].”
• Efficient Spectral Feature Selection with Minimum Redundancy (2010, methodology): “In recent years, researchers designed spectral feature selection algorithms (He, Cai, and Niyogi 2005; Zhao and Liu 2007) to identify relevant features through evaluating features’ capability on preserving sample similarity.”
• Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, methodology): “Instead of considering k-nearest neighbor or ε-ball-based methods as in typical graph construction [147], Xu et al.”
• A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data (2015, methodology): “[21] proposed to encode data locality via a Laplacian Score (LS) for feature ranking.”
• Learning ordinal discriminative features for age estimation (2012, methodology): “In addition, RankBoost and LAR have better performance than Laplacian Score and Fisher Score.”
• A Survey of Modern Questions and Challenges in Feature Extraction (2015, methodology): “Filter methods select a subset of features based on the notion of an importance score that is computed independent of the classifier (Gu et al., 2012; Boutsidis et al., 2009; He et al., 2005; Chen and Lin, 2006).”
• Differential evolution for filter feature selection based on information theory and feature ranking (2018, methodology): “Another simple filter approach is Laplacian Score [33] which does not only consider features with larger variances, but also considers the features with stronger locality preserving ability.”
• A Graphbased Framework for Multi-Task Multi-View Learning (2011, methodology): “Figure 2 shows the comparison results of PALM with Laplacian score method [14], feature variance method (selecting the features with the largest variance), CRO [18], and random sampling.”
• A Robust-Equitable Measure for Feature Ranking and Selection (2017, methodology): “Individual search methods (Guyon and Elisseeff, 2003; He et al., 2005) evaluate each feature individually according to a criterion and then select features, which either satisfies a condition or are top-ranked.”
• Feature Selection (2016, background): “According to He et al. (2005), Fisher Score can be considered as a special case of Laplacian Score as long as the affinity matrix is S(i, j ) = { 1 nl if yi = yj = l 0 otherwise, .”
• Feature selection for text classification: A review (2018, background): “…117, 118, 122], feature selection with streaming features [103], online feature selection [99], filter-based locality preserving feature selection [36], similarity preserving feature selection [112], feature selection with optimization techniques [105], regularization based feature selection…”
• Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (2014, background): “Laplacian Score [21] is proposed to select those features which can best reflect the underlying manifold structure.”
• A review of unsupervised feature selection methods (2019, methodology): “2006), Laplacian Score (LS) (He et al. 2005), SPEC (Zhao and Liu 2007), and USFSM (Solorio-Fernández et al.”
• Pattern Recognition (2018, unknown): “In this setting, viewpoint changes can be alleviated since we can find any corresponding angle for a probe.”