An Efficient Greedy Method for Unsupervised Feature Selection

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How researchers see this paper

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FEED: Feature-level Ensemble for Knowledge Distillation (2019, methodology): “Reconstruction errors were used as a criterion for feature selection or PCAs in (Boutsidis, Mahoney, and Drineas 2008; Farahat, Ghodsi, and Kamel 2011; Li, Tang, and Liu 2017; Masaeli et al. 2010), where they use linear models.”
Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization (2014, background): “JRL integrates the sparse and semisupervised regularization for all the media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type [14], [15].”
Feature Selection (2016, background): “GreedyFS [Farahat et al. 2011] uses a projection matrix to project the original data onto the span of some representative feature vectors and derives an efficient greedy algorithm to obtain these representative features.”
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, methodology): “GreedyFS [Farahat et al., 2011] uses a projection matrix to project the original data onto the span of some representative feature vectors.”
Feature-Level Ensemble Knowledge Distillation for Aggregating Knowledge from Multiple Networks (2020, methodology): “Reconstruction errors were used as a criterion for feature selection or PCAs in [2, 5, 16, 17], where they use linear models.”