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Sparse Exponential Family Principal Component Analysis

We propose a Sparse exponential family Principal Component Analysis (SePCA) method suitable for any type of data following exponential family distributions, to achieve simultaneous dimension reduction and variable selection for better interpretation of the results. Because of the generality of expon...

Täydet tiedot

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Bibliografiset tiedot
Julkaisussa:Pattern Recognit
Päätekijät: Lu, Meng, Huang, Jianhua Z., Qian, Xiaoning
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: 2016
Aiheet:
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC5210214/
https://ncbi.nlm.nih.gov/pubmed/28066030
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.patcog.2016.05.024
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