Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information Changwei Hu1 Piyush Rai12 Lawrence Carin1

Source URL: www.cse.iitk.ac.in

Use of Semi-separable Approximate Factorization and Direction-preserving for Constructing Effective Preconditioners X. Sherry Li Lawrence Berkeley National Laboratory Ming Gu (UC Berkeley)

Source URL: crd-legacy.lbl.gov

• Numerical linear algebra
• Matrices
• Linear algebra
• Matrix theory
• Operator theory
• Cholesky decomposition
• Singular value decomposition
• Matrix
• Schur complement
• Triangular matrix
• Rank
• Hierarchical matrix

Diagnostic cluster analysis of mathematics skills Yoon Soo Park and Young-Sun Lee Teachers College, Columbia University, New York, USA Clustering and similarity trees are effective techniques for grouping and visualizing

Source URL: ierinstitute.org

• Educational research
• Multivariate statistics
• Psychometrics
• Trends in International Mathematics and Science Study
• Bioinformatics
• Attribute hierarchy method
• Cluster analysis
• Hierarchical clustering
• K-means clustering
• Distance matrix
• Multidimensional scaling
• Mahalanobis distance

SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering Han Zhao† , Pascal Poupart† , Yongfeng Zhang§ and Martin Lysy‡ † David R. Cheriton School of Computer Science, University of Waterloo, Canada D

Source URL: yongfeng.me

• Multivariate statistics
• Linear algebra
• Matrix theory
• Non-negative matrix factorization
• Cluster analysis
• Spectral clustering
• K-means clustering
• Document clustering
• Pattern recognition
• Expectationmaximization algorithm
• Determining the number of clusters in a data set
• Hierarchical clustering

Towards an Optimal-Order Approximate Sparse Factorization Exploiting Data-Sparseness in Separators X. Sherry Li, Lawrence Berkeley National Laboratory Artem Napov, Université Libre de Bruxelles Francois-Henry Rouet, Law

Source URL: crd-legacy.lbl.gov

• Rapid transit in Austria
• Vienna U-Bahn
• U1
• U3
• Matrix
• Nested dissection
• Hierarchical matrix
• U4

Details of our Motif Clustering Procedure 1 Bayesian Hierarchical Clustering Model The data for each discovered motif is a count matrix Ni which can have different widths and number of counts compared to other TF motifs.

Source URL: www-stat.wharton.upenn.edu

• Hierarchical clustering
• Network analysis
• Cluster analysis
• Probability distributions

Solving Hierarchical Diffusion Equation Using Some Matrix Algebra A. Radyna Mechanical and Mathematical Faculty, Belarusian State University 4 Skaryna ave., Minsk, Belarus e-mail : ales.radyna gmail.com k−1

Source URL: p-adics2015.matf.bg.ac.rs

LEARNING TO SEGMENT SONGS WITH ORDINAL LINEAR DISCRIMINANT ANALYSIS Brian McFee Daniel P.W. Ellis Center for Jazz Studies

Source URL: bmcfee.github.io

• Statistics
• Multivariate statistics
• Data analysis
• Mathematics
• Image segmentation
• Cluster analysis
• Hierarchical clustering
• Non-negative matrix factorization
• Self-similarity matrix
• Market segmentation
• Principal component analysis
• Pattern recognition

Proceedings, The Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-15) Bayesian Clustering of Player Styles for Multiplayer Games Aline Normoyle Shane T. Jensen

Source URL: www-stat.wharton.upenn.edu

• Statistics
• Computing
• Mathematics
• Cluster analysis
• K-means clustering
• Hierarchical clustering
• Non-negative matrix factorization
• Fuzzy clustering
• Machine learning
• Computer cluster
• Dirichlet process
• Consensus clustering

Research Paper The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS Qiuju ZHOU1, Fuhai LENG1† & Loet LEYDESDORFF2 National Science Library, Chinese Academy of Sciences, Beijing, Chi

Source URL: 159.226.100.150

• Multivariate statistics
• Bioinformatics
• Matrix theory
• Factor analysis
• Matrices
• SPSS
• Hierarchical clustering
• Cluster analysis
• Matrix
• Distance matrix
• Co-occurrence matrix
• Similarity measure