This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it.

The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include: Quantized compressed sensing Classification Machine learning Oracle inequalities Non-convex optimization Image reconstruction Statistical learning theory This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.

This volume contains revised versions of 43 papers presented during the 21st Annual Conference of the Gesellschaft fur Klassifikation (GfKl), the German Classification Society. The conference took place at the University of Pots- dam (Germany) in March 1997; the local organizer was Prof. 1. Balderjahn, Chair of Business Administration and Marketing at Potsdam. The scientific program of the conference included 103 plenary and con- tributed papers, software and book presentations as well as special (tutorial) courses. Researchers and practitioners interested in data analysis and clus- tering methods, information sciences and database techniques, and in the main topic of the conference: data highways and their importance for classifi- cation and data analysis, had the opportunity to discuss recent developments and to establish cross-disciplinary cooperation in these fields. The conference owed much to its sponsors - Berliner Volksbank - Daimler Benz AG - Deutsche Telekom AG Direktion Potsdam - Dresdner Bank AG Filiale Potsdam - Henkel KGaA - Landeszentralbank in Berlin und Brandenburg - Ministerium fur Wissenschaft, Forschung und Kultur des Landes Brandenburg - Sci con GmbH - Siemens AG - Universitat Potsdam - Unternehmensgruppe Roland Ernst who helped in many ways. Their generous support is gratefully acknowl- edged. In the present proceedings volume, selected and peer-reviewed papers are presented in six chapters as follows.

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

Das vorliegende Buch entstand aus einer Reihe von Vorlesungen, die wir an der Rheinisch-Westfcilischen Technischen Hochschule Aachen, der European Business School, der Universitat Oldenburg und der Universitat Augsburg seit 1984 ge- halt en haben. Diese Vorlesungen wandten sich vor allem an Informatikstudenten und Mathematikstudenten mit Nebenfach Informatik mit dem Ziel, stochastische Grundbegriffe unter besonderer Beriicksichtigung Informatik-spezifischer Aspekte zu vermitteln. Unter den zahlreichen Einsatzfeldern stochastischer Methoden in der Informatik seien hier beispielhaft genannt: Die Average-Case-Analyse von Algorithmen, die stochastische Automatentheorie, Anwendungen im Bereich des CAD (Bezier-Kurven und -Flii.chen), stochastische Informationstheorie und Codierungstheorie, Rechnernetze und Leistungsbewer- tung von Rechnersystemen (Warteschlangenprobleme), Bildverarbeitung (Compu- tertomographie), automatische Spracherkennung (Hidden-Markov-Modelle), Ex- pertensysteme (effiziente Bereclmung von bedingten Wahrscheinlichkeiten), kiinst- liche Intelligenz (Neuronale Netze), stochastische Optimierungs- und Suchverfah- ren (Simulated Annealing), stochastische Simulation, probabilistische Algorithmen u.v.a .. Die zum Verstiindnis benotigten theoretischen Grundlagen, die erfahrungsgemiill haufig weit iiber den in einfiihrenden Veranstaltungen angebotenen Stoff hinausge- hen, sind dementsprechend vielfci.ltig und reichen von einfachen kombinatorischen Uberlegungen bei einigen Problemen der Average-Case-Analyse von Algorithmen bis hin zu tiefliegenden Satzen der axiomatischen Wahrscheinlichkeitstheorie, etwa bei den Markoff-Ketten und -Prozessen oder der Theorie der Punktprozesse im Bereich der Bildverarbeitung.

This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it.