software . 2018

Linear Optimal Low-Rank Projection

Bridgeford, Eric W; Tang, Minh; Yim, Jason; Vogelstein, Joshua T;
Open Access
  • Published: 15 May 2018
  • Publisher: Zenodo
Abstract
<p>Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsuperv...
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Zenodo
Software . 2018
Provider: Datacite
Zenodo
Software . 2018
Provider: Datacite
Zenodo
Software . 2018
Provider: Zenodo
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software . 2018

Linear Optimal Low-Rank Projection

Bridgeford, Eric W; Tang, Minh; Yim, Jason; Vogelstein, Joshua T;