國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Algorithmic advances in Riemannian g...

Minh, Ha Quang.

Algorithmic advances in Riemannian geometry and applicationsfor machine learning, computer vision, statistics, and optimization /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithmic advances in Riemannian geometry and applicationsedited by Ha Quang Minh, Vittorio Murino.
其他題名:	for machine learning, computer vision, statistics, and optimization /
其他作者:	Minh, Ha Quang.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xiv, 208 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Geometry, Riemannian.
電子資源:	http://dx.doi.org/10.1007/978-3-319-45026-1
ISBN:	9783319450261$q(electronic bk.)

Algorithmic advances in Riemannian geometry and applicationsfor machine learning, computer vision, statistics, and optimization /
Algorithmic advances in Riemannian geometry and applicationsfor machine learning, computer vision, statistics, and optimization /[electronic resource] :edited by Ha Quang Minh, Vittorio Murino. - Cham :Springer International Publishing :2016. - xiv, 208 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

Introduction -- Bayesian Statistical Shape Analysis on the Manifold of Diffeomorphisms -- Sampling Constrained Probability Distributions using Spherical Augmentation -- Geometric Optimization in Machine Learning -- Positive Definite Matrices: Data Representation and Applications to Computer Vision -- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings -- Dictionary Learning on Grassmann Manifolds -- Regression on Lie Groups and its Application to Affine Motion Tracking -- An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures.

This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.

ISBN: 9783319450261$q(electronic bk.)

Standard No.: 10.1007/978-3-319-45026-1doiSubjects--Topical Terms:

191082
Geometry, Riemannian.

LC Class. No.: QA671

Dewey Class. No.: 516.373

Algorithmic advances in Riemannian geometry and applicationsfor machine learning, computer vision, statistics, and optimization /
LDR:02987nmm a2200325 a 4500 001 498228
003 DE-He213
005 20161005090611.0
006 m d
007 cr nn 008maaau
008 170511s2016 gw s 0 eng d
020 $a 9783319450261$q(electronic bk.)
020 $a 9783319450254$q(paper)
024 7 $a 10.1007/978-3-319-45026-1 $2 doi
035 $a 978-3-319-45026-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA671
072 7 $a UYQP $2 bicssc
072 7 $a COM016000 $2 bisacsh
082 0 4 $a 516.373 $2 23
090 $a QA671 $b .A396 2016
245 0 0 $a Algorithmic advances in Riemannian geometry and applications $h [electronic resource] : $b for machine learning, computer vision, statistics, and optimization / $c edited by Ha Quang Minh, Vittorio Murino.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiv, 208 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a Introduction -- Bayesian Statistical Shape Analysis on the Manifold of Diffeomorphisms -- Sampling Constrained Probability Distributions using Spherical Augmentation -- Geometric Optimization in Machine Learning -- Positive Definite Matrices: Data Representation and Applications to Computer Vision -- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings -- Dictionary Learning on Grassmann Manifolds -- Regression on Lie Groups and its Application to Affine Motion Tracking -- An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures.
520 $a This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.
650 0 $a Geometry, Riemannian. $3 191082
650 0 $a Riemannian manifolds. $3 190949
650 0 $a Machine learning. $3 188639
650 0 $a Computer vision. $3 200113
650 0 $a Statistics. $3 182057
650 0 $a Optimization $3 252886
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Mathematical Applications in Computer Science. $3 530811
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
700 1 $a Minh, Ha Quang. $3 761242
700 1 $a Murino, Vittorio. $3 577015
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 559645
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-45026-1
950 $a Computer Science (Springer-11645)