Everything Totally Explained


Ask & we'll explain, totally!
Correlation dimension
Totally Explained


  NEW! All the latest news in the worlds of computer gaming, entertainment, the environment,  
finance, health, politics, science, stocks & shares, technology and much, much, more.  


View this entry using RSS

Everything about Correlation Dimension totally explained

In chaos theory the correlation dimension (denoted by ν) is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension). For example, if we've a set of random points on the real number line between 0 and 1, the correlation dimension will be ν=1, while if they're distributed on say, a triangle embedded in 3-space (or m-space), the correlation dimension will be ν=2. This is what we'd intuitively expect from a measure of dimension. The real utility of the correlation dimension is in determining the (possibly fractional) dimensions of fractal objects. There are other methods of measuring dimension (for example the Hausdorff dimension, the box-counting dimension, and the information dimension) but the correlation dimension has the advantage of being straightforwardly and quickly calculated, and is often in agreement with other calculations of dimension.
   For any set of N points in an m-dimensional space » vec x(i)=[x_

where g is the total number of pairs of points which have a distance between them that's less than distance varepsilon (a graphical representation of such close pairs is the recurrence plot). As the number of points tends to infinity, and the distance between them tends to zero, the correlation integral, for small values of varepsilon, will take the form:
» C(varepsilon) sim varepsilon^ u

If the number of points is sufficiently large, and evenly distributed, a Log-log graph of the correlation integral versus varepsilon will yield an estimate of ν. This idea can be qualitatively understood by realizing that for higher dimensional objects, there will be more ways for points to be close to each other, and so the number of pairs close to each other will rise more rapidly for higher dimensions.
   Grassberger and Procaccia introduced the technique in 1983, the method was used to show that the number of sunspots on the sun, after accounting for the known cycles such as the daily and 11-year cycles, is very likely not random noise, but rather chaotic noise, with a low-dimensional fractal attractor.

Further Information

Get more info on 'Correlation Dimension'.


External Link Exchanges

Do you know how hard it is to get a link from a large encyclopaedia? Well we're different and will prove it. To get a link from us just add the following HTML to your site on a relevant page:

    <a href="http://correlation_dimension.totallyexplained.com">Correlation dimension Totally Explained</a>

Then simply click through this link from your web page. Our crawlers will verify your link, extract the title of your web page and instantly add a link back to it. If you like you can remove the words Totally Explained and embed the link in article text.
   As long as your link remains in place, we'll keep our link to you right here. Please play fair - our crawlers are watching. Your site must be closely related to this one's topic. Any kind of spamming, dubious practises or removing the link will result in your link from us being dropped and, potentially, your whole site being banned.



Copyright © 2007-8 totallyexplained.com | Licensed under the GNU Free Documentation License | Site Map
This article contains text from the Wikipedia article Correlation dimension (History) and is released under the GFDL | RSS Version