Learning Distance Function by Coding Similarity
published: June 23, 2007, recorded: June 2007, views: 503
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 consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. "similar" point pairs. We define the similarity in information theoretic terms, as the gain in coding length when shifting from independent encoding of the pair to joint encoding. Under simple Gaussian assumptions, this formulation leads to a non-Mahalanobis similarity function which is effcient and simple to learn. This function can be viewed as a likelihood ratio test, and we show that the optimal similaritypreserving pro jection of the data is a variant of Fisher Linear Discriminant. We also show that under some naturally occurring sampling conditions of equivalence constraints, this function converges to a known Mahalanobis distance (RCA). The suggested similarity function exhibits superior performance over alternative Mahalanobis distances learnt from the same data. Its superiority is demonstrated in the context of image retrieval and graph based clustering, using a large number of data sets.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !