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Low-rank and sparse modeling for visual analysis [electronic resource]

자료유형

E-Book(소장)

개인저자

Fu, Yun.

서명 / 저자사항

Low-rank and sparse modeling for visual analysis [electronic resource] / Yun Fu, editor.

발행사항

Cham : Springer International Publishing : Imprint: Springer, 2014.

형태사항

1 online resource (vii, 236 p.) : ill. (some col.).

ISBN

9783319120003

요약

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. Covers the most state-of-the-art topics of sparse and low-rank modeling Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis Contributions from top experts voicing their unique perspectives included throughout.

일반주기

Title from e-Book title page.

내용주기

Nonlinearly Structured Low-Rank Approximation -- Latent Low-Rank Representation -- Scalable Low-Rank Representation -- Low-Rank and Sparse Dictionary Learning -- Low-Rank Transfer Learning -- Sparse Manifold Subspace Learning -- Low Rank Tensor Manifold Learning -- Low-Rank and Sparse Multi-Task Learning -- Low-Rank Outlier Detection -- Low-Rank Online Metric Learning.

서지주기

Includes bibliographical references and index.

이용가능한 다른형태자료

Issued also as a book.

일반주제명

Optical pattern recognition. Image analysis.

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245	0 0	▼a Low-rank and sparse modeling for visual analysis ▼h [electronic resource] / ▼c Yun Fu, editor.
260		▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2014.
300		▼a 1 online resource (vii, 236 p.) : ▼b ill. (some col.).
500		▼a Title from e-Book title page.
504		▼a Includes bibliographical references and index.
505	0	▼a Nonlinearly Structured Low-Rank Approximation -- Latent Low-Rank Representation -- Scalable Low-Rank Representation -- Low-Rank and Sparse Dictionary Learning -- Low-Rank Transfer Learning -- Sparse Manifold Subspace Learning -- Low Rank Tensor Manifold Learning -- Low-Rank and Sparse Multi-Task Learning -- Low-Rank Outlier Detection -- Low-Rank Online Metric Learning.
520		▼a This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. Covers the most state-of-the-art topics of sparse and low-rank modeling Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis Contributions from top experts voicing their unique perspectives included throughout.
530		▼a Issued also as a book.
538		▼a Mode of access: World Wide Web.
650	0	▼a Optical pattern recognition.
650	0	▼a Image analysis.
700	1	▼a Fu, Yun.
856	4 0	▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-12000-3
945		▼a KLPA
991		▼a E-Book(소장)

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중앙도서관

No.	소장처	청구기호	등록번호	도서상태	반납예정일	예약	서비스
No. 1	소장처 중앙도서관/e-Book 컬렉션/	청구기호 CR 006.42	등록번호 E14032646	도서상태 대출불가(열람가능)	반납예정일	예약	서비스 M

Low-rank and sparse modeling for visual analysis [electronic resource]

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