Understanding High-Dimensional Spaces

by David B. Skillicorn

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...

• Format: Paperback
• Language: English
• ISBN: 3642333974
• ISBN13: 9783642333972
• Release Date: September 2012
• Publisher: Springer
• Length: 108 Pages
• Weight: 0.40 lbs.
• Dimensions: 0.4" x 6.1" x 9.0"