Data

Sparse Principal Component Analysis with Preserved Sparsity Pattern

The University of Western Australia
Seghouane, Abd-Krim ; Shokouhi, Navid ; Koch, Inge
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.24433/co.4593141.v1&rft.title=Sparse Principal Component Analysis with Preserved Sparsity Pattern&rft.identifier=10.24433/co.4593141.v1&rft.publisher=Code Ocean&rft.description=MATLAB code + demo to reproduce results for Sparse Principal Component Analysis with Preserved Sparsity. This code calculates the principal loading vectors for any given high-dimensional data matrix. The advantage of this method over existing sparse-PCA methods is that it can produce principal loading vectors with the same sparsity pattern for any number of principal components. Please see Readme.md for more information.&rft.creator=Seghouane, Abd-Krim &rft.creator=Shokouhi, Navid &rft.creator=Koch, Inge &rft.date=2019&rft.relation=http://research-repository.uwa.edu.au/en/publications/fbe0a676-a360-4c90-b915-c1ec06c1bb97&rft_subject=machine-learning&rft_subject=sparse&rft_subject=Computer Science&rft_subject=principal-component-analysis&rft_subject=Algorithm&rft.type=dataset&rft.language=English Access the data
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MATLAB code + demo to reproduce results for "Sparse Principal Component Analysis with Preserved Sparsity". This code calculates the principal loading vectors for any given high-dimensional data matrix. The advantage of this method over existing sparse-PCA methods is that it can produce principal loading vectors with the same sparsity pattern for any number of principal components. Please see Readme.md for more information.

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Associated Persons
Abd-Krim Seghouane (Creator); Navid Shokouhi (Creator)

Issued: 2019-02-14

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Algorithm | Computer Science | machine-learning | principal-component-analysis | sparse |

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