Sparse principal component regression via singular value decomposition approach
نویسندگان
چکیده
منابع مشابه
Biclustering via sparse singular value decomposition.
Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse...
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l=1 σlulv T l (1) ∀ l σl ∈ R, σl ≥ 0 (2) ∀ l, l 〈ul, ul′〉 = 〈vl, vl′〉 = δ(l, l) (3) To prove this consider the matrix AA ∈ R. Set ul to be the l’th eigenvector of AA . By definition we have that AAul = λlul. Since AA T is positive semidefinite we have λl ≥ 0. Since AA is symmetric we have that ∀ l, l 〈ul, ul′〉 = δ(l, l). Set σl = √ λl and vl = 1 σl Aul. Now we can compute the following: 〈vl, vl...
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A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented...
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در این پایان نامه به معرفی روشهای مختلف محاسبه psvd می پردازیم. بخشی از این روشها به بررسی روشهای مختلف محاسبه psvd در مقالات مطالعه شده می پردازد که می توان به محاسبهpsvd با استفاده از الگوریتمهای pqrd و pevd و sbr2 و محاسبه psvd براساس تکنیک kogbetliantz و روش پارامتریک برای محاسبه psvd اشاره نمود. بخش بعدی نیز به بررسی روشهای مستقیم پیشنهادی محاسبه psvd برای ماتریسهای 2×2و2× n و n×2 و 3× n و...
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ژورنال
عنوان ژورنال: Advances in Data Analysis and Classification
سال: 2021
ISSN: 1862-5347,1862-5355
DOI: 10.1007/s11634-020-00435-2