عنوان

Spectral clustering and biclustering :

شابک

1118650689 (electronic bk.)

شابک

1118650700 (electronic bk.)

شابک

9781118650684 (electronic bk.)

شابک

9781118650707 (electronic bk.)

شابک اشتباه

111865059X

شابک اشتباه

1118650719

شابک اشتباه

9781118344927 (hardback)

شابک اشتباه

9781118650592

شابک اشتباه

9781118650714

شماره

b429305

عنوان اصلي

Spectral clustering and biclustering :

نام عام مواد

[Book]

ساير اطلاعات عنواني

learning large graphs and contingency tables /

نام نخستين پديدآور

Marianna Bolla

تاريخ

1309

نام خاص و کميت اثر

1 online resource (xxiii, 268 pages)

متن يادداشت

Includes bibliographical references and index

متن يادداشت

Machine generated contents note: Dedication Preface Acknowledgements List of Abbreviations Introduction 1 Multivariate analysis techniques for representing graphs and contingency tables 1.1 Quadratic placement problems for weighted graphs and hypergraphs 1.1.1 Representation of edge-weighted graphs 1.1.2 Representation of hypergraphs 1.1.3 Examples for spectra and representation of simple graphs 1.2 SVD of contingency tables and correspondence matrices 1.3 Normalized Laplacian and modularity spectra 1.4 Representation of joint distributions 1.4.1 General setup 1.4.2 Integral operators between L2 spaces 1.4.3 When the kernel is the joint distribution itself 1.4.4 Maximal correlation and optimal representations 1.5 Treating nonlinearities via reproducing kernel Hilbert spaces 1.5.1 Notion of the reproducing kernel 1.5.2 RKHS corresponding to a kernel 1.5.3 Two examples of an RKHS 1.5.4 Kernel -- based on a sample -- and the empirical feature map References 2 Multiway cut problems 2.1 Estimating multiway cuts via spectral relaxation 2.1.1 Maximum, minimum, and ratio cuts of edge-weighted graphs 2.1.2 Multiway cuts of hypergraphs 2.2 Normalized cuts 2.3 The isoperimetric number and sparse cuts 2.4 The Newman-Girvan modularity 2.4.1 Maximizing the balanced Newman-Girvan modularity 2.4.2 Maximizing the normalized Newman-Girvan modularity 2.4.3 Anti-community structure and some examples 2.5 Normalized bicuts of contingency tables References 3 Large networks, perturbation of block structures 3.1 Symmetric block structures burdened with random noise 3.1.1 General blown-up structures 3.1.2 Blown-up multipartite structures 3.1.3 Weak links between disjoint components 3.1.4 Recognizing the structure 3.1.5 Random power law graphs and the extended planted partition model 3.2 Noisy contingency tables 3.2.1 Singular values of a noisy contingency table 3.2.2 Clustering the rows and columns via singular vector pairs 3.2.3 Perturbation results for correspondence matrices 3.2.4 Finding the blown-up skeleton 3.3 Regular cluster pairs 3.3.1 Normalized modularity and volume regularity of edgeweighted graphs 3.3.2 Correspondence matrices and volume regularity of contingency tables 3.3.3 Directed graphs References 4 Testable graph and contingency table parameters 4.1 Convergent graph sequences 4.2 Testability of weighted graph parameters 4.3 Testability of minimum balanced multiway cuts 4.4 Balanced cuts and fuzzy clustering 4.5 Noisy graph sequences 4.6 Convergence of the spectra and spectral subspaces 4.7 Convergence of contingency tables References 5 Statistical learning of networks 5.1 Parameter estimation in random graph models 5.1.1 EMalgorithmfor estimating the parameters of the block model 5.1.2 Parameter estimation in the _ and _ models 5.2 Nonparametric methods for clustering networks 5.2.1 Spectral clustering of graphs and biclustering of contingency tables 5.2.2 Clustering of hypergraphs 5.3 Supervised learning References A Linear algebra and some functional analysis A.1 Metric, normed vector, and Euclidean spaces A.2 Hilbert spaces A.3 Matrices References B Random vectors and matrices B.1 Random vectors B.2 Random matrices References C Multivariate statistical methods C.1 Principal Component Analysis C.2 Canonical Correlation Analysis C.3 Correspondence Analysis C.4 Multivariate Regression and Analysis of Variance C.5 The k-means clustering C.6 Multidimensional Scaling C.7 Discriminant Analysis References Index

بدون عنوان

8

متن يادداشت

"Offering a timely and novel treatment of spectral clustering and biclustering of networks, this book bridges the gap between graph theory and statistics by giving answers to the demanding questions that arise when statisticians are confronted with large weighted graphs or rectangular arrays. The author presents a wide range of classical and modern statistical methods adapted to weighted graphs and contingency tables. In addition, practical examples from social and biological networks are included, and a teacher's guide is provided on a supporting website"--

متن يادداشت

"Provides a timely, novel and unified treatment of many important problems surrounding the spectral and classification properties of networks"--

عنوان

Spectral clustering and biclustering.

شماره استاندارد بين المللي کتاب و موسيقي

9781118344927

موضوع مستند نشده

Contingency tables

موضوع مستند نشده

Graph theory

موضوع مستند نشده

Multivariate analysis

موضوع مستند نشده

MAT-- 007000

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Bolla, Marianna

مستند نام تنالگان تاييد نشده

Ohio Library and Information Network

مستند نام تنالگان تاييد نشده

Wiley Online Library (Online service)

تاريخ عمليات

20141129034557.0

قواعد فهرست نويسي ( بخش توصيفي )

rda

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y