7 citations · 10 across the 7 of their papers we have counts for
7 papers
A Convex formulation for linear discriminant analysis
Sai Vijay Kumar Surineela, Prathyusha Kanakamalla, Harigovind Harikumar +1
We present a supervised dimensionality reduction technique called Convex Linear Discriminant Analysis (ConvexLDA). The proposed model optimizes a multi-objective cost function by b…
A Multi-Domain Multi-Task Approach for Feature Selection from Bulk RNA Datasets
Karim Salta, Tomojit Ghosh, Michael Kirby
In this paper a multi-domain multi-task algorithm for feature selection in bulk RNAseq data is proposed. Two datasets are investigated arising from mouse host immune response to Sa…
Sparse Linear Centroid-Encoder: A Convex Method for Feature Selection
Tomojit Ghosh, Michael Kirby, Karim Karimov
We present a novel feature selection technique, Sparse Linear Centroid-Encoder (SLCE). The algorithm uses a linear transformation to reconstruct a point as its class centroid and,…
Feature Selection using Sparse Adaptive Bottleneck Centroid-Encoder
Tomojit Ghosh, Michael Kirby
We introduce a novel nonlinear model, Sparse Adaptive Bottleneck Centroid-Encoder (SABCE), for determining the features that discriminate between two or more classes. The algorithm…
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder
Tomojit Ghosh, Michael Kirby
We propose a new supervised dimensionality reduction technique called Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder (CE) \citep{…
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection
Tomojit Ghosh, Michael Kirby
Autoencoders have been widely used as a nonlinear tool for data dimensionality reduction. While autoencoders don't utilize the label information, Centroid-Encoders (CE)\cite{ghosh2…