Robust Representation for Data Analytics

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.

Aceasta carte introduce conceptele si modelele de invatare robusta a reprezentarii si ofera un set de solutii pentru a face fata sarcinilor de analiza a datelor din lumea reala, cum ar fi clusterizarea, clasificarea, modelarea seriilor temporale, detectarea valorilor anterioare, filtrarea colaborativa, detectarea comunitatii etc. sunt dezvoltate tipuri de reprezentari robuste ale caracteristicilor, care extind intelegerea graficului, a subspaiului si a dictionarului. Utilizand teoria modelarii slabe si rare, autorii dezvolta reprezentari robuste ale caracteristicilor sub diferite paradigme de invatare, inclusiv invatare nesupravegheata, invatare supravegheata, semi -invatare supravegheata, invatare cu mai multe vizualizari, invatare prin transfer si invatare profunda. Reprezentari robuste pentru analiza datelor acopera o gama larga de aplicatii in domeniile de cercetare ale datelor mari, calcule centrate pe om, recunoasterea tiparelor, marketing digital, web mining si viziune computerizata.