Bag Dissimilarities for Multiple Instance Learning
published: Oct. 17, 2011, recorded: September 2011, views: 3314
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.
When objects cannot be represented well by single feature vectors, a collection of feature vectors can be used. This is what is done in Multiple Instance learning, where it is called a bag of instances. By using a bag of instances, an object gains more internal structure than when a single feature vector is used. This improves the expressiveness of the representation, but also adds complexity to the classification of the object. This paper shows that for the situation that not a single instance determines the class label of a bag, simple bag dissimilarity measures can significantly outperform standard multiple instance classifiers. In particular a measure that computes just the average minimum distance between instances, or a measure that uses the Earth Mover’s distance, perform very well.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !