# Download Understanding High-Dimensional Spaces (SpringerBriefs in Computer Science) fb2

**David B. Skillicorn**

- Author:David B. Skillicorn
- ISBN:3642333974
- ISBN13:978-3642333972
- Genre:
- Publisher:Springer; 2012 edition (September 27, 2012)
- Pages:108 pages
- Subcategory:Computer Science
- Language:
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- Rating:4.5
- Votes:969
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Prof David B. Skillicorn. SpringerBriefs in Computer Science.

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. David B. Skillicorn is a professor in the School of Computing at Queen's University in Kingston, Ontario; he is also an adjunct professor in the Mathematics and Computer Science Department of the Royal Military College of Canada. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect.

from book Understanding High-Dimensional Spaces (p. 3-98). Chapter · September 2012 with 3 Reads. In all of the previous chapters, we have assumed, perhaps implicitly, that understanding high-dimensional spaces was something that happened in isolation, and only once for each particular dataset. Nothing could be further from the truth. The process of exploring and understanding a dataset is always iterative, and the results of each round, and the deeper understanding that comes from it, inform the strategy and tactics of the next round.

Thinking about High-Dimensional Spaces But in a high dimensional space, every image is mixed together and surrounded . Machine learning and data science with mathematical flavoring.

Thinking about High-Dimensional Spaces. In a way, we already have a way to visualize an 1024-dimensional space: 32-by-32 grayscale images! This may seem a bit circular, but it works for some situations. As we move through this high dimensional space, the pixel intensities in our image will change. But in a high dimensional space, every image is mixed together and surrounded by noisy, meaningless nonsense. Pixel Space and Latent Space. The high-dimensional representation we’ve been discussing so far is often referred to as an ambient space (or pixel space in the case of images).

Understanding High-Dimensional Spaces book. This book proposes new ways of thinking about high-dimensional spaces using two models: the skeleton that relates the clusters to one another, and the boundaries in empty space that provide new perspectives on outliers and on outlying regions.

Understanding high-dimensional spaces. Berlin ; New York : Springer, c2012. Series: SpringerBriefs in computer science. Subjects: Data mining.

High-Dimensional Probability The reader can do them immediately to check his or her understanding of the material just presented, and to prepare. better for later developments.

High-Dimensional Probability. An Introduction with Applications in Data Science. Roman Vershynin University of California, Irvine. What is this book about? High-dimensional probability is an area of probability theory that studies random objects in Rn where the dimension n can be very large. This book places par-ticular emphasis on random vectors, random matrices, and random projections. The reader can do them immediately to check his or her understanding of the material just presented, and to prepare. The diculty of the exercises is indicated by the.

Understanding Computer Science for Advanced Level By Ray Bradley

Understanding Computer Science for Advanced Level By Ray Bradley. Customs services and international tracking provided. Understanding dBASE III Plus (Sybex Computer Books) Simpson, Alan Paperback.

High-dimensional spaces arise as a way of modelling datasets with many . Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored.

SpringerBriefs in Computer Science 2191-5768.

Publisher country is United States of America. SpringerBriefs in Computer Science 2191-5768. The scientific journal is included in the Scopus database.

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect.

There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets are large and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions.

The book will be of value to practitioners, graduate students and researchers.