Projection pursuit with applications to scRNA sequencing data

12/16/2019
by   Elvis Cui, et al.
0

In this paper, we explore the limitations of PCA as a dimension reduction technique and study its extension, projection pursuit (PP), which is a broad class of linear dimension reduction methods. We first discuss the relevant concepts and theorems and then apply PCA and PP (with negative standardized Shannon's entropy as the projection index) on single cell RNA sequencing data.

READ FULL TEXT

page 7

page 8

research
05/30/2020

direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods

The direpack package aims to establish a set of modern statistical dimen...
research
05/25/2018

On the Estimation of Entropy in the FastICA Algorithm

The fastICA algorithm is a popular dimension reduction technique used to...
research
06/09/2021

Large-scale optimal transport map estimation using projection pursuit

This paper studies the estimation of large-scale optimal transport maps ...
research
08/26/2022

Tangent phylogenetic PCA

Phylogenetic PCA (p-PCA) is a version of PCA for observations that are l...
research
09/04/2015

Minimum Spectral Connectivity Projection Pursuit

We study the problem of determining the optimal low dimensional projecti...
research
12/23/2019

Asymptotic Behavior for Textiles in von-Kármán regime

The paper is dedicated to the investigation of simultaneous homogenizati...
research
11/22/2019

2SDR: Applying Kronecker Envelope PCA to denoise Cryo-EM Images

Principal component analysis (PCA) is arguably the most widely used dime...

Please sign up or login with your details

Forgot password? Click here to reset