Convex Principal Feature Selection

55 citations recorded by the source corpus.

How researchers see this paper

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

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.”
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.”
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, methodology): “Convex Principle Feature Selection (CPFS) [Masaeli et al., 2010] reformulates the feature selection problem as a convex continuous optimization problem that minimizes a mean-squared-reconstruction error with linear and sparsity constraint.”
An Efficient Greedy Method for Unsupervised Feature Selection (2011, methodology): “However, Masaeli et al. [3] showed that these sparse coefficients may be distributed across different features and accordingly are not always useful for feature selection.”
Feature Selection (2016, methodology): “Among them, Convex Principal Feature Selection (CPFS) [Masaeli et al. 2010] reformulates the feature selection problem as a convex continuous optimization problem that minimizes a mean-squared-reconstruction error with linear and sparsity constraint.”