A Compressive Classification Framework for High-Dimensional Data

A Compressive Classification Framework for High-Dimensional Data

We propose a compressive classification framework for settings where the data dimensionality is significantly larger than the sample size. The proposed method, referred to as compressive regularized discriminant analysis (CRDA), is based on linear discriminant analysis and has the ability to select...

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Bibliographic Details
Main Authors: Muhammad Naveed Tabassum, Esa Ollila
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Classification
covariance matrix estimation
discriminant analysis
feature selection
high-dimensional statistics
joint-sparse recovery
Online Access:https://ieeexplore.ieee.org/document/9258370/

Internet

https://ieeexplore.ieee.org/document/9258370/

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