![Robust representation for data analytics [electronic resource] : models and applications](https://image.aladin.co.kr/product/10907/64/cover/331960175x_2.jpg)
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| 001 | 000045991984 | |
| 005 | 20190731154000 | |
| 006 | m d | |
| 007 | cr | |
| 008 | 190726s2017 sz a ob 001 0 eng d | |
| 020 | ▼a 9783319601755 | |
| 020 | ▼a 9783319601762 (e-book) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 050 | 4 | ▼a QA76.9.D343 |
| 082 | 0 4 | ▼a 006.332 ▼2 23 |
| 084 | ▼a 006.332 ▼2 DDCK | |
| 090 | ▼a 006.332 | |
| 100 | 1 | ▼a Li, Sheng. |
| 245 | 1 0 | ▼a Robust representation for data analytics ▼h [electronic resource] : ▼b models and applications / ▼c Sheng Li, Yun Fu. |
| 260 | ▼a Cham : ▼b Springer, ▼c c2017. | |
| 300 | ▼a 1 online resource (xi, 224 p.) : ▼b ill. | |
| 490 | 1 | ▼a Advanced Information and Knowledge Processing, ▼x 1610-3947 |
| 500 | ▼a Title from e-Book title page. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a Introduction -- Fundamentals of Robust Representations -- Part 1: Robust Representation Models -- Robust Graph Construction -- Robust Subspace Learning -- Robust Multi-View Subspace Learning -- Part 11: Applications -- Robust Representations for Collaborative Filtering -- Robust Representations for Response Prediction -- Robust Representations for Outlier Detection -- Robust Representations for Person Re-Identification -- Robust Representations for Community Detection -- Index. |
| 520 | ▼a This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a Artificial intelligence. |
| 650 | 0 | ▼a Optical pattern recognition. |
| 650 | 0 | ▼a Computer vision. |
| 700 | 1 | ▼a Fu, Yun. |
| 830 | 0 | ▼a Advanced Information and Knowledge Processing. |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-60176-2 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 006.332 | 등록번호 E14015836 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.
Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
New feature
This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
정보제공 :
목차
Introduction.- Fundamentals of Robust Representations.- Part 1: Robust Representation Models.- Robust Graph Construction.- Robust Subspace Learning.- Robust Multi-View Subspace Learning.- Part 11: Applications.- Robust Representations for Collaborative Filtering.- Robust Representations for Response Prediction.- Robust Representations for Outlier Detection.- Robust Representations for Person Re-Identification.- Robust Representations for Community Detection.- Index.
정보제공 :