TY - BOOK ID - 16132963 TI - Robust Representation for Data Analytics : Models and Applications AU - Li, Sheng. AU - Fu, Yun. PY - 2017 SN - 3319601768 331960175X PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Knowledge representation (Information theory) KW - Big data. KW - Data sets, Large KW - Large data sets KW - Representation of knowledge (Information theory) KW - Computer science. KW - Data mining. KW - Artificial intelligence. KW - Image processing. KW - Pattern recognition. KW - Computer Science. KW - Data Mining and Knowledge Discovery. KW - Artificial Intelligence (incl. Robotics). KW - Pattern Recognition. KW - Image Processing and Computer Vision. KW - Artificial intelligence KW - Information theory KW - Data sets KW - Optical pattern recognition. KW - Computer vision. KW - Artificial Intelligence. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Machine vision KW - Vision, Computer KW - Image processing KW - Pattern recognition systems KW - Optical data processing KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - Optical data processing. KW - Optical computing KW - Visual data processing KW - Integrated optics KW - Photonics KW - Computers KW - Design perception KW - Pattern recognition KW - Form perception KW - Perception KW - Figure-ground perception KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:16132963 AB - 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. ER -