172 citations · 228 across the 24 of their papers we have counts for
7 papers · 1 filter
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…
Locally Linear Attributes of ReLU Neural Networks
Ben Sattelberg, Renzo Cavalieri, Michael Kirby +2
A ReLU neural network determines/is a continuous piecewise linear map from an input space to an output space. The weights in the neural network determine a decomposition of the inp…
Supervised Dimensionality Reduction and Visualization using Centroid-encoder
Tomojit Ghosh, Michael Kirby
Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label i…
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
Henry Kvinge, Elin Farnell, Michael Kirby +1
Dimensionality-reduction methods are a fundamental tool in the analysis of large data sets. These algorithms work on the assumption that the "intrinsic dimension" of the data is ge…