Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
The Journal of Machine Learning Research
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
A density-based method for adaptive LDA model selection
Neurocomputing
Determining Automatically the Size of Learned Ontologies
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Diversifying Product Review Rankings: Getting the Full Picture
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Pervasive and Mobile Computing
Unsupervised latent concept modeling to identify query facets
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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It is important to identify the “correct” number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M1 and M2 as given by Cd*w = M1d*t x Qt*w. Where d is the number of documents present in the corpus and w is the size of the vocabulary. The quality of the split depends on “t”, the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics – this is shown by a 'dip' at the right value for 't'.