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Found 6 papers, 0 papers with code
Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection
In the first step, we solve the linear Centroid-Encoder, a convex optimization problem over a matrix $A$.
Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder
During training, we update class centroids by taking the Hadamard product of the centroids and weights of the sparse layer, thus ignoring the irrelevant features from the target.
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder
SLCE works by mapping the samples of a class to its class centroid using a linear transformation.
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection
The resulting algorithm, Sparse Centroid-Encoder (SCE), extracts discriminatory features in groups using a sparsity inducing $\ell_1$-norm while mapping a point to its class centroid.
Robust Feature Selection using Sparse Centroid-Encoder
We develop a sparse optimization problem for the determination of the total set of features that discriminate two or more classes.
Supervised Dimensionality Reduction and Visualization using Centroid-encoder
The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space.
