Dimensionality Reduction

Madhuri R. Marri; Lakshmi Ramachandran; Pradeep Murukannaiah; Padmashree Ravindra; Amrita Paul; Da Young Lee; David Funk; Shanmugapriya Murugappan; William Hendrix

doi:10.1201/b15352-14

Dimensionality Reduction

Madhuri R. Marri, Lakshmi Ramachandran, Pradeep Murukannaiah, Padmashree Ravindra, Amrita Paul, Da Young Lee, David Funk, Shanmugapriya Murugappan, William Hendrix

Department of Computer Science

North Carolina State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

both curse [8]-a blessing because it implies a lot of information, and a curse because the data might contain a lot of irrelevant information and be tougher to analyze. High-dimensional data is known to pose a wide variety of challenges in the field of statistics. Dimensionality Reduction, or DR, is therefore employed to deal with the issue of high-dimensional data. Specifically, DR is primarily applied when there is a need to: 1. Select only relevant features from the given data (feature selection) or 2. Extract lower-dimensional data from the current form of the data because the current form is too difficult to analyze (feature extraction).

Original language	English
Title of host publication	Practical Graph Mining with R
Pages	263-310
Number of pages	48
ISBN (Electronic)	9781439860854
DOIs	https://doi.org/10.1201/b15352-14
State	Published - 1 Jan 2013

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AU - Lee, Da Young

AU - Funk, David

AU - Murugappan, Shanmugapriya

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Dimensionality Reduction

Abstract

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