Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity
published: Sept. 25, 2016, recorded: August 2016, views: 1409
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.
This paper introduces the ﬁrst generic version of data dependent dissimilarity and shows that it provides a better closest match than distance measures for three existing algorithms in clustering, anomaly detection and multi-label classiﬁcation. For each algorithm, we show that by simply replacing the distance measure with the data dependent dissimilarity measure, it overcomes a key weakness of the otherwise unchanged algorithm.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !