Error Analysis For Matrix Elastic-net Regularization Algorithms
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von GoogleAnmeldenAusgeblendete FelderBooksbooks.google.de - Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and...https://books.google.de/books/about/Handbook_of_Robust_Low_Rank_and_Sparse_M.html?hl=de&id=T1WzDAAAQBAJ&utm_source=gb-gplus-shareHandbook of Robust Low-Rank and Sparse Matrix DecompositionMeine BücherHilfeErweiterte BuchsucheE-Book kaufen - 150,49 €Nach Druckexemplar suchenCRC PressAmazon.deBuch.deBuchkatalog.deLibri.deWeltbild.deAlle Händler»Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video ProcessingThierry Bouwmans, Necdet Serhat Aybat, El-hadi ZahzahCRC Press, 06.07.2016 http://www.ncbi.nlm.nih.gov/pubmed/24806123 - 520 Seiten 0 Rezensionenhttps://books.google.de/books/about/Handbook_of_Robust_Low_Rank_and_Sparse_M.html?hl=de&id=T1WzDAAAQBAJHandbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop https://books.google.com/books?id=T1WzDAAAQBAJ&pg=SA13-PA18&lpg=SA13-PA18&dq=error+analysis+for+matrix+elastic-net+regularization+algorithms&source=bl&ots=XdE2E-QcqU&sig=ovmEE6WhH__Qtak6rzumYl29Ytw&hl=en&sa=X&ved=0ahUKE access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining. Voransicht des Buches » Was andere dazu sagen-Rezensi
Recovery.Neural Comput 2016 Mar 6;28(3):525-62. Epub 2016 Jan 6.Yunlong Feng, Shao-Gao Lv, Hanyuan Hang, Johan A K Suykens Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic http://www.pubpdf.com/pub/26735744/Kernelized-Elastic-Net-Regularization-Generalization-Bounds-and-Sparse-Recovery net regularization (Zou & Hastie, 2005). The kernel in KENReg is not http://dl.acm.org/citation.cfm?id=2913376 required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. Full Text Link Source Status http://www.mitpressjournals.org/doi/10.1162/NECO_a_00812Publisher SiteFound Similar Publications Oct2014 Learning rates error analysis of lq coefficient regularization learning with gaussian kernel.Neural Comput 2014 Oct 24;26(10):2350-78. Epub 2014 Jul 24.Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and l(q) regularization schemes with 0 < q < ∞ are central in use. It is known that different q leads to different error analysis for properties of the deduced estimators, say, l(2) regularization leads to a smooth estimator, while l(1) regularization leads to a sparse estimator. Then how the generalization capability of l(q) regularization learning varies with q is worthy of investigation. View Full Text PDF Listings View primary source full text article PDFs. May2012 Error analysis for matrix elastic-net regularization algorithms.IEEE Trans Neural Netw Learn Syst 2012 May;23(5):737-48Hong Li, Na Chen, Luoqing Li Elastic-net regularization is a successful approach in statistical modeling. It can avoid large variations which occur in estimating complex models. In this paper, elastic-net regularization is extended to a more general setting, the matrix recovery (matrix completion) setting. View Full Text PDF Listings View primary source full text article PDFs. Jun2015 Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces.Neural Comput 2015 Jun 31;27(6):1294-320. Epub 2015 Mar 31.Shao-Gao Lv Gradient learning (GL), initially proposed by Mukherjee and Zhou (2006) has been proved to be a powerful tool for conducting variable selection and dimensional reduction simultaneously. This approach presents a nonparametric version of a gradient estimator with po
Months): n/a ·Downloads (cumulative): n/a ·Citation Count: 0 Published in: ·Journal Neural Computation archive Volume 28 Issue 3, March 2016 Pages 525-562 MIT Press Cambridge, MA, USA tableofcontents doi>10.1162/NECO_a_00812 Tools and Resources TOC Service: Email RSS Save to Binder Export Formats: BibTeX EndNote ACMRef Share: | Contact Us | Switch to single page view (no tabs) **Javascript is not enabled and is required for the "tabbed view" or switch to the single page view** Powered by The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2016 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Reader QuickTime Windows Media Player Real Player Did you know the ACM DL App is now available? Did you know your Organization can subscribe to the ACM Digital Library? The ACM Guide to Computing Literature All Tags Export Formats Save to Binder