Variance of the subgraph count for sparse Erd˝ os-R´ enyi graphs Robert B. Ellis a,1,∗ , James P. Ferry b,1 a Department

Source URL: math.iit.edu

• Graph theory
• Morphisms
• Mathematical logic
• Model theory
• Structure
• Universal algebra
• Graph isomorphism
• Two-graph
• Covering graph

Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning † Joel C. McCall† , David Wipf‡ , Mohan M. Trivedi† , Bhaskar Rao‡

Source URL: cvrr.ucsd.edu

• Lane
• Intelligent transportation system
• Conference on Computer Vision and Pattern Recognition
• Bayesian inference

MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

Source URL: www.bioinf.jku.at

• Signal processing
• Statistical models
• Convex optimization
• Operations research
• Independent component analysis
• Wavelet
• Linear programming
• Support vector machine
• Matrix
• Machine learning
• Sparse approximation
• Sparse dictionary learning

Modeling multi-way data can be accomplished using tensors, which are data structures indexed along three or more dimensions. Tensor factorization is increasingly used to analyze extremely large and sparse multi-way datas

Source URL: mmds-data.org

• Tensors
• Tensor
• OpenMP
• Graph factorization
• Splatt
• Tensor software

Localized Matrix Factorization for Recommendation based on Matrix Block Diagonal Forms Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, Shi Feng Tsinghua University, Beijing, China

Source URL: yongfeng.me

• Sparse matrices
• Matrix theory
• Matrices
• Block matrix
• Matrix
• Sparse matrix
• Non-negative matrix factorization
• Graph partition

Highly Accurate 3D Surface Models by Sparse Surface Adjustment Michael Ruhnke Rainer K¨ummerle Abstract— In this paper, we propose an approach to obtain

Source URL: europa.informatik.uni-freiburg.de

• Artificial intelligence
• Robot navigation
• Simultaneous localization and mapping
• 3D scanner
• Kinect
• Computer vision
• Quantization

Mach Learn:3–39 DOIs10994A majorization-minimization approach to the sparse generalized eigenvalue problem Bharath K. Sriperumbudur · David A. Torres ·

Source URL: eceweb.ucsd.edu

• Multivariate statistics
• Operations research
• Mathematical optimization
• Numerical linear algebra
• Dimension reduction
• Sparse PCA
• Sparse approximation
• Principal component analysis
• Limit of a function
• Convex optimization
• Semidefinite programming
• K-SVD

HIERATIC WP3:
 
 Algorithms for Identifying
 Linear Coarse Grainings


Source URL: www.hieratic.eu

• Linear algebra
• Matrix theory
• Sparse matrices
• Matrices
• Matrix
• Eigenvalues and eigenvectors
• Diagonal matrix
• Invariant subspace
• Invariant
• Block matrix

1030 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 3, MARCH 2006 Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise

Source URL: users.cms.caltech.edu

• Linear algebra
• Convex analysis
• Mathematical optimization
• Numerical linear algebra
• Signal processing
• Sparse approximation
• Convex function
• Norm
• Vector space
• Banach space
• Preconditioner
• Convex set

Stable principal component pursuit (SPCP) seeks to decompose noisy signals into their underlying low-rank and sparse components. In this study, we introduce two new methods to parallelize SPCP in order to take advantage

Source URL: mmds-data.org