Sparse PCA

Sparse PCA

An extension of the classic principal component analysis (PCA) method that offers dimensionality reduction of data with better statistical properties and interpretability than classic PCA.

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Edits on 8 March, 2019
Daniel Frumkin"Added image, description, article, categories, and related topics."
Daniel Frumkin edited on 8 March, 2019 4:05 pm
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Sparse PCA

An extension of the classic principal component analysis (PCA) method that offers dimensionality reduction of data with better statistical properties and interpretability than classic PCA.

Article

Sparse principal component analysis (Sparse PCA) is an extension of the classic principal component analysis (PCA) method that offers dimensionality reduction of data with better statistical properties and interpretability than classic PCA.

One of the disadvantages of classic PCA is that the principal components are linear combinations of all variables. In other words, the principal components depend on all of the original variables. Sparce PCA extends traditional PCA by finding linear combinations that contain only a few input variables.

For some problems, this means that Sparce PCA will produce similar results as traditional PCA, but with simpler and more interpretable components.

For example, in "A Direct Formulation for Sparse PCA Using Semidefinite Programming" (D'Aspremont et al. (2007)), 500 genes were measures for a large number of samples. With traditional PCA, the factors obtained each use all 500 genes, making the results difficult to interpret. Using Sparse PCA, the factors altogether only involved 14 genes and the data was more interpretable.

People

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Related Golden topics

Hui Zou

Creator

Robert Tibshirani

Creator

Trevor Hastie

Creator

Further reading

Title
Author
Link
Type

Everything you did and didn't know about PCA · Its Neuronal

Alex Williams

Web

Sparse Principal Component Analysis

Hui Zou, Trevor Hastie, Robert Tibshirani

Sparse Principal Component Analysis: Algorithms and Applications

Youwei Zhang

Academic paper

Documentaries, videos and podcasts

Title
Date
Link

Sparse PCA in High Dimensions

December 18, 2013

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Edits on 7 August, 2018
Golden AI"Linkify text links in standard tables"
Golden AI edited on 7 August, 2018 6:56 pm
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Further reading

Author
Title
Link
Type

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Auto-Encoders and Sparse PCA

http://arxiv.org/abs/1502.06626v1http://arxiv.org/abs/1502.06626v1

Academic paper

Edits on 5 June, 2018
Tianchang He
Tianchang He edited on 5 June, 2018 8:33 pm
Edits made to:
Further reading (+5/-5 characters)

Sparse PCA

An extension of the classic principal component analysis (PCA) method that offers dimensionality reduction of data with better statistical properties and interpretability than classic PCA.

Further reading

Author
Title
Link
Type

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Auto-Encoders and Sparse PCA

http://arxiv.org/abs/1502.06626v1

Academic Paperpaper

Edits on 1 June, 2018
Golden AI"Merging standard tables"
Golden AI edited on 1 June, 2018 3:37 am
Edits made to:
Academic papers (-1 rows) (-3 cells) (-122 characters)
Further reading (+1 rows) (+4 cells) (+136 characters)
Academic papers

Author
Title
Link

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Auto-Encoders and Sparse PCA

http://arxiv.org/abs/1502.06626v1

Further reading

Author
Title
Link
Type

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Auto-Encoders and Sparse PCA

http://arxiv.org/abs/1502.06626v1

Academic Paper

Edits on 14 April, 2018
Jude Gomila
Jude Gomila edited on 14 April, 2018 4:35 pm
Academic papers

Author
Title
Link

Malik Magdon-Ismail, Christos Boutsidis

Optimal Sparse Linear Auto-Encoders and Sparse PCA

http://arxiv.org/abs/1502.06626v1

Edits on 1 January, 2017
Golden AI"Initial topic creation"
Golden AI created this topic on 1 January, 2017 12:00 am
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 Sparse PCA

An extension of the classic principal component analysis (PCA) method that offers dimensionality reduction of data with better statistical properties and interpretability than classic PCA.

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