• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 31
Next 10 →

Robust principal component analysis?

by Emmanuel J Candès , Xiaodong Li , Yi Ma , John Wright - Journal of the ACM, , 2011
"... Abstract This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the ..."
Abstract - Cited by 569 (26 self) - Add to MetaCart
-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the 1 norm. This suggests the possibility of a principled approach to robust principal component

Robust nonnegative matrix factorization via l1 norm regularization,” Arxiv preprint arXiv:1204.2311

by Bin Shen, Luo Si, Rongrong Ji, Baodi Liu , 2012
"... Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as face recognition, motion segmentation, etc. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two no ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as face recognition, motion segmentation, etc. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two

Chapter 12 Rough Sets and Rough Logic: A KDD Perspective

by Zdzis Law Pawlak, Lech Polkowski, Andrzej Skowron
"... Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations of con ..."
Abstract - Add to MetaCart
Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations

© Author(s) 2015. CC Attribution 3.0 License.

by T. J. Troy, M. Konar, V. Srinivasan, S. Thompson , 2015
"... www.hydrol-earth-syst-sci.net/19/3667/2015/ doi:10.5194/hess-19-3667-2015 ..."
Abstract - Add to MetaCart
www.hydrol-earth-syst-sci.net/19/3667/2015/ doi:10.5194/hess-19-3667-2015

RICE UNIVERSITY Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing

by Jason Noah Laska , 2011
"... The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
Abstract - Add to MetaCart
The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order

POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR

by Dominique Tschopp, Prof E. Telatar, Prof S. Diggavi, Prof M. Grossglauser, Prof J. -y, Le Boudec, Prof M. Mitzenmacher, Prof S. Shakkottai
"... 2010 to my wife, Joyce, and my family...- Résumé- ..."
Abstract - Add to MetaCart
2010 to my wife, Joyce, and my family...- Résumé-

1C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework

by Pablo Sprechmann, Guillermo Sapiro, Yonina C. Eldar
"... Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an `1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso ..."
Abstract - Add to MetaCart
of the Lasso at the individual feature level, with the block-sparsity property of the Group Lasso, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured. This results in the Hierarchical Lasso (HiLasso), which shows important practical advantages. We

Scenario Generation and Reduction for Long-term and Short-term Power System Generation Planning under Uncertainties

by Yonghan Feng, James D. Mccalley, William Q. Meeker, Jo Min, Lizhi Wang
"... ii ..."
Abstract - Add to MetaCart
Abstract not found

Guaranteeing Communication Quality in Real World WSN Deployments

by Fbk-irst Bruno, Kessler Foundation, Matteo Ceriotti, Dr. Amy, L. Murphy, Bruno Kessler Foundation (fbk-irst, Amy L. Murphy, Prof Prabal Dutta, Prof Koen Langendoen, Prof Leo Selavo
"... April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by: ..."
Abstract - Add to MetaCart
April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by:

AND MATHEMATICAL ENGINEERING

by Madeleine Udell, Professor Lester Mackey , 2015
"... ii ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract not found
Next 10 →
Results 1 - 10 of 31
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University