img Leseprobe Leseprobe

Robust Representation for Data Analytics

Models and Applications

Yun Fu, Sheng Li

PDF
ca. 117,69
Amazon iTunes Thalia.de Weltbild.de Hugendubel Bücher.de ebook.de kobo Osiander Google Books Barnes&Noble bol.com Legimi
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.

Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik

Beschreibung

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.

Weitere Titel zum gleichen Preis
Cover Edge AI
Xiaofei Wang
Cover Microservices
Victor Rivera
Cover Information Storage
Cornelia S. Große

Kundenbewertungen

Schlagwörter

Outlier Detection, Graph Construction, Subspace Learning, Robust Representations, Multi-view Learning