A study of the optimality of PCA under spectral sparsification

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URI
http://hdl.handle.net/20.500.14066/3730
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Autor(es)
Mercado, Sergio; Villagra Riquelme, Marcos DanielAutoridad CONACYT
Fecha de publicación
2018
Tipo de publicación
info:eu-repo/semantics/conferencePaper
Materia(s)
Resumen
Principal component analisys (PCA) is a data analysis technique for mapping points in Rn to a two or three dimensional space. This dimensionality reduction preserves the natural grouping of points and information of data.
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