Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection

• URL: http://arxiv.org/abs/2201.12910v1
• Date: Sun, 30 Jan 2022 20:46:24 GMT
• Title: Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection
• Authors: Tomojit Ghosh and Michael Kirby
• Abstract summary: We develop a sparse implementation of the centroid-encoder for nonlinear data reduction and visualization called Centro Sparseid-Encoder. We also provide a feature selection framework that first ranks each feature by its occurrence, and the optimal number of features is chosen using a validation set. The algorithm is applied to a wide variety of data sets including, single-cell biological data, high dimensional infectious disease data, hyperspectral data, image data, and speech data.
• Score: 1.2487990897680423
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We develop a sparse optimization problem for the determination of the total set of features that discriminate two or more classes. This is a sparse implementation of the centroid-encoder for nonlinear data reduction and visualization called Sparse Centroid-Encoder (SCE). We also provide a feature selection framework that first ranks each feature by its occurrence, and the optimal number of features is chosen using a validation set. The algorithm is applied to a wide variety of data sets including, single-cell biological data, high dimensional infectious disease data, hyperspectral data, image data, and speech data. We compared our method to various state-of-the-art feature selection techniques, including two neural network-based models (DFS, and LassoNet), Sparse SVM, and Random Forest. We empirically showed that SCE features produced better classification accuracy on the unseen test data, often with fewer features.

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