Robust Representation for Data Analytics
Springer International Publishing (Verlag)
978-3-319-60175-5 (ISBN)
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.
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.
Erscheinungsdatum | 08.09.2017 |
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Reihe/Serie | Advanced Information and Knowledge Processing |
Zusatzinfo | XI, 224 p. 52 illus., 49 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 521 g |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Schlagworte | Artificial Intelligence • artificial intelligence (incl. robotics) • Computer Science • computer vision • Data Mining • data mining and knowledge discovery • Expert systems / knowledge-based systems • Graph Construction • Image Processing • image processing and computer vision • Multi-view learning • Multi-view Lewarning • Outlier Detection • pattern recognition • Robotics • Robust Representations • subspace learning |
ISBN-10 | 3-319-60175-X / 331960175X |
ISBN-13 | 978-3-319-60175-5 / 9783319601755 |
Zustand | Neuware |
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