東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Composite applications (Computer science)" ]

切換: 標籤 | MARC模式 | ISBD

Dictionary learning algorithms and a...

Dumitrescu, Bogdan.

FindBook

Google Book

Amazon

博客來

Dictionary learning algorithms and applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dictionary learning algorithms and applications/ by Bogdan Dumitrescu, Paul Irofti.
作者:	Dumitrescu, Bogdan.
其他作者:	Irofti, Paul.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xiv, 284 p. :ill., digital ;24 cm.
內容註:	Chapter1: Sparse representations -- Chapter2: Dictionary learning problem -- Chapter3: Standard algorithms -- Chapter4: Regularization and incoherence -- Chapter5: Other views on the DL problem -- Chapter6: Optimizing dictionary size -- Chapter7: Structured dictionaries -- Chapter8: Classification -- Chapter9: Kernel dictionary learning -- Chapter10: Cosparse representations.
Contained By:	Springer eBooks
標題:	Algorithms. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-78674-2
ISBN:	9783319786742

Dictionary learning algorithms and applications
Dumitrescu, Bogdan.

Dictionary learning algorithms and applications[electronic resource] /by Bogdan Dumitrescu, Paul Irofti. - Cham :Springer International Publishing :2018. - xiv, 284 p. :ill., digital ;24 cm.

Chapter1: Sparse representations -- Chapter2: Dictionary learning problem -- Chapter3: Standard algorithms -- Chapter4: Regularization and incoherence -- Chapter5: Other views on the DL problem -- Chapter6: Optimizing dictionary size -- Chapter7: Structured dictionaries -- Chapter8: Classification -- Chapter9: Kernel dictionary learning -- Chapter10: Cosparse representations.

This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.

ISBN: 9783319786742

Standard No.: 10.1007/978-3-319-78674-2doiSubjects--Topical Terms:

536374
Algorithms.

LC Class. No.: QA9.58 / .D85 2018

Dewey Class. No.: 518.1

Dictionary learning algorithms and applications
LDR:02985nmm a2200337 a 4500 001 2142118
003 DE-He213
005 20181112143208.0
006 m d
007 cr nn 008maaau
008 181214s2018 gw s 0 eng d
020 $a 9783319786742 $q (electronic bk.)
020 $a 9783319786735 $q (paper)
024 7 $a 10.1007/978-3-319-78674-2 $2 doi
035 $a 978-3-319-78674-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA9.58 $b .D85 2018
072 7 $a TTBM $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a COM073000 $2 bisacsh
082 0 4 $a 518.1 $2 23
090 $a QA9.58 $b .D888 2018
100 1 $a Dumitrescu, Bogdan. $3 3227138
245 1 0 $a Dictionary learning algorithms and applications $h [electronic resource] / $c by Bogdan Dumitrescu, Paul Irofti.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiv, 284 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter1: Sparse representations -- Chapter2: Dictionary learning problem -- Chapter3: Standard algorithms -- Chapter4: Regularization and incoherence -- Chapter5: Other views on the DL problem -- Chapter6: Optimizing dictionary size -- Chapter7: Structured dictionaries -- Chapter8: Classification -- Chapter9: Kernel dictionary learning -- Chapter10: Cosparse representations.
520 $a This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
650 0 $a Algorithms. $3 536374
650 0 $a Computer algorithms. $3 523872
650 0 $a Composite applications (Computer science) $3 3321471
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Mathematical and Computational Engineering. $3 3226306
650 2 4 $a Circuits and Systems. $3 896527
650 2 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Information Systems and Communication Service. $3 891044
700 1 $a Irofti, Paul. $3 3321470
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-78674-2
950 $a Engineering (Springer-11647)