TY - BOOK ID - 7838994 TI - Open problems in spectral dimensionality reduction AU - Strange, Harry. AU - Zwiggelaar, Reyer PY - 2014 SN - 3319039423 3319039431 PB - Cham [Switzerland] : Springer, DB - UniCat KW - Database management. KW - Dimension reduction (Statistics) KW - Dimensional analysis. KW - Dimensionality reduction (Statistics) KW - Reduction, Dimension (Statistics) KW - Reduction, Dimensionality (Statistics) KW - Computer science. KW - Data structures (Computer science). KW - Algorithms. KW - Artificial intelligence. KW - Image processing. KW - Computer Science. KW - Artificial Intelligence (incl. Robotics). KW - Data Structures. KW - Algorithm Analysis and Problem Complexity. KW - Image Processing and Computer Vision. KW - Statistics KW - Physical measurements KW - Data structures (Computer scienc. KW - Computer software. KW - Computer vision. KW - Artificial Intelligence. KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Software, Computer KW - Computer systems 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 - Data structures (Computer science) KW - Information structures (Computer science) KW - Structures, Data (Computer science) KW - Structures, Information (Computer science) KW - File organization (Computer science) KW - Abstract data types (Computer science) KW - Optical data processing. KW - Optical computing KW - Visual data processing KW - Integrated optics KW - Photonics KW - Computers KW - Algorism KW - Algebra KW - Arithmetic KW - Optical equipment KW - Foundations KW - Computer science KW - Mathematics. UR - https://www.unicat.be/uniCat?func=search&query=sysid:7838994 AB - The last few years have seen a great increase in the amount of data available to scientists. Datasets with millions of objects and hundreds, if not thousands of measurements are now commonplace in many disciplines. However, many of the computational techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects, or measurements, whilst retaining important information inherent to the data. Spectral dimensionality reduction is one such family of methods that has proven to be an indispensable tool in the data processing pipeline. In recent years the area has gained much attention thanks to the development of nonlinear spectral dimensionality reduction methods, often referred to as manifold learning algorithms. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. Those wishing to use spectral dimensionality reduction without prior knowledge of the field will immediately be confronted with questions that need answering: What parameter values to use? How many dimensions should the data be embedded into? How are new data points incorporated? What about large-scale data? For many, a search of the literature to find answers to these questions is impractical, as such, there is a need for a concise discussion into the problems themselves, how they affect spectral dimensionality reduction, and how these problems can be overcome. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work. ER -