Robust Representation for Data Analytics

Models and Applications
Softcover Nachdruck of the original 1. Auflage 2017
ISBN: 978-3-319-86796-0
Verlag: Springer

Buch, Englisch, 224 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 408 g

Reihe: Advanced Information and Knowledge Processing

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.

Zielgruppe

Research

Autoren/Hrsg.

Li, Sheng

Fu, Yun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.