Deep Learning with Yacine on MSN

Visualizing High-Dimensional Data Using PCA in Scikit-Learn

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...
JSTOR Daily

A MINIMUM DISCREPANCY APPROACH WITH FOURIER TRANSFORM IN SUFFICIENT DIMENSION REDUCTION

We propose an optimal family of estimators in sufficient dimension reduction using a Fourier transform based on a quadratic discrepancy function. Our proposed approach has advantages over existing ...
JSTOR Daily

PRINCIPAL SUPPORT VECTOR MACHINES FOR LINEAR AND NONLINEAR SUFFICIENT DIMENSION REDUCTION

We introduce a principal support vector machine (PSVM) approach that can be used for both linear and nonlinear sufficient dimension reduction. The basic idea is to divide the response variables into ...
CU Boulder News & Events

Dimension Reduction and Classification Using PCA, Factor Analysis and Discriminant Functions - A Short Overview

Tuesday, October 28: Often researchers are faced with data in very high dimensions (e.g. too many predictors for a regression model), or must come up with a rule to classify data in pre-determined ...