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Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.
5
votes
Accepted
Inferring Relational Hierarchies of Words
Look up taxonomy/ontology construction/induction. Relevant papers:
Automatic Taxonomy Construction from Keywords via Scalable Bayesian Rose Trees
Topic Models for Taxonomies
OntoLearn Reloaded. A Gr …
1
vote
Accepted
About A technique mentioned on ESL to transform an unsupervised into supervised
Fascinating problem, welcome to the site! In the second edition, this is covered in section 14.2.4 Unsupervised as Supervised Learning. The idea here is to design a classifier (that's supervised learn …
1
vote
How to create a social network like IBM's Watson News Explorer?
They've created a graph from the news articles, topics, and named entities (locations, persons, companies, organizations). There are a lot of things going on here, but k-means is not one of them. If I …
2
votes
Machine Learning with sometimes missing data
If only a small fraction of features is missing you can use imputation. For more serious cases, you can use a probabilistic model such as a Gaussian process, which will let you marginalize the missing …
4
votes
generalized likelihood ratio test (GLRT)
Likelihood-ratio tests are a mainstay of classical hypothesis testing. The idea is to form the likelihoods of the two hypotheses under consideration, and choose the one with the highest likelihood if …