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Author Strange, Harry, author.

Title Open problems in spectral dimensionality reduction / Harry Strange, Reyer Zwiggelaar.

Publication Info. Heidelberg : Springer, [2014]


Location Call No. OPAC Message Status
 Axe Books 24x7 IT E-Book  Electronic Book    ---  Available
Description 1 online resource.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Series SpringerBriefs in computer science
SpringerBriefs in computer science.
Bibliography Includes bibliographical references and index.
Contents Introduction -- Spectral Dimensionality Reduction -- Modelling the Manifold -- Intrinsic Dimensionality -- Incorporating New Points -- Large Scale Data -- Postcript.
Summary 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.
Note Print version record.
Subject Dimension reduction (Statistics)
MATHEMATICS -- Probability & Statistics -- General.
Dimension reduction (Statistics) (OCoLC)fst01749911
Computer Science.
Artificial Intelligence (incl. Robotics).
Data Structures.
Algorithm Analysis and Problem Complexity.
Image Processing and Computer Vision.
Genre/Form Electronic books.
Added Author Zwiggelaar, Reyer, author.
Other Form: Print version: Strange, Harry. Open problems in spectral dimensionality reduction 3319039423 (OCoLC)864358417
ISBN 9783319039435 (electronic bk.)
3319039431 (electronic bk.)
Standard No. 10.1007/978-3-319-03943-5 doi
AU@ 000059665890
AU@ 000060583801
DEBBG BV043607679
DKDLA 820120-katalog:000661132
NLGGC 373030282
NZ1 15579839
NZ1 15737634

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