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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates)
/Language (en\055US)
/Created (2011)
/Description-Abstract (Principal Components Analysis\176\050PCA\051 is often used as a feature extraction procedure\056 Given a matrix \044X \134in \134mathbb\173R\175\136\173n \134times d\175\044\054 whose rows represent \044n\044 data points with respect to \044d\044 features\054 the top \044k\044 right singular vectors of \044X\044 \050the so\055called \134textit\173eigenfeatures\175\051\054 are arbitrary linear combinations of all available features\056 The eigenfeatures are very useful in data analysis\054 including the regularization of linear regression\056 Enforcing sparsity on the eigenfeatures\054 i\056e\056\054 forcing them to be linear combinations of only a \134textit\173small\175 number of actual features \050as opposed to all available features\051\054 can promote better generalization error and improve the interpretability of the eigenfeatures\056 We present deterministic and randomized algorithms that construct such sparse eigenfeatures while \134emph\173provably\175 achieving in\055sample performance comparable to regularized linear regression\056 Our algorithms are relatively simple and practically efficient\054 and we demonstrate their performance on several data sets\056)
/Producer (Python PDF Library \055 http\072\057\057pybrary\056net\057pyPdf\057)
/Title (Sparse Features for PCA\055Like Linear Regression)
/Date (2011)
/Type (Conference Proceedings)
/firstpage (2285)
/Book (Advances in Neural Information Processing Systems 24)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (J\056 Shawe\055Taylor and R\056S\056 Zemel and P\056L\056 Bartlett and F\056 Pereira and K\056Q\056 Weinberger)
/Author (Christos Boutsidis\054 Petros Drineas\054 Malik Magdon\055Ismail)
/lastpage (2293)
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