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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.

Knowledge representation (Information theory) --- Big data. --- Data sets, Large --- Large data sets --- Representation of knowledge (Information theory) --- Computer science. --- Data mining. --- Artificial intelligence. --- Image processing. --- Pattern recognition. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence (incl. Robotics). --- Pattern Recognition. --- Image Processing and Computer Vision. --- Artificial intelligence --- Information theory --- Data sets --- Optical pattern recognition. --- Computer vision. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical equipment

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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.

Mathematical statistics --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- patroonherkenning --- beeldverwerking --- factoranalyse --- datamining --- big data --- deep learning --- KI (kunstmatige intelligentie) --- data acquisition --- optica

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Semiconductors --- Semiconductors --- Silicon-on-insulator technology --- Design and construction --- Electric properties