Multiclass-multilabel classification with more labels than examples

author: Ofer Dekel, Microsoft Research
published: June 3, 2010,   recorded: May 2010,   views: 4755


Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.


We discuss multiclass-multilabel classification problems in which the set of possible labels is extremely large. Most existing multiclass-multilabel learning algorithms expect to observe a reasonably large sample from each class, and fail if they receive only a handful of examples with a given label. We propose and analyze the following two-stage approach: first use an arbitrary (perhaps heuristic) classification algorithm to construct an initial classifier, then apply a simple but principled method to augment this classifier by removing harmful labels from its output. A careful theoretical analysis allows us to justify our approach under some reasonable conditions (such as label sparsity and power-law distribution of label frequencies), even when the training set does not provide a statistically accurate representation of most classes. Surprisingly, our theoretical analysis continues to hold even when the number of classes exceeds the sample size. We demonstrate the merits of our approach on the ambitious task of categorizing the entire web using the 1.5 million categories defined on Wikipedia.

See Also:

Download slides icon Download slides: aistats2010_dekel_mmcw_01.pdf (529.1┬áKB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Mathieu, November 19, 2010 at 11:42 a.m.:

Just for the record, Wikipedia categories are not free text.

Write your own review or comment:

make sure you have javascript enabled or clear this field: