A simple feature extraction for high dimensional image representations
published: Feb. 25, 2007, recorded: February 2005, views: 5227
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We investigate a method to find local clusters in low dimensional subspaces of high dimensional data, e.g. in high dimensional image descriptions. Using cluster centers instead of the full set of data will speed up the performance of learning algorithms for object recognition, and will possibly also improve performance because overfitting might be avoided.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !