%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{NIPS-2010, Workshop on Practical Applications of Sparse Modeling:Open Issues and New Directions_2010,
         author = {Varadarajan, Jagannadan and Emonet, Remi and Odobez, Jean-Marc},
       projects = {Idiap, SNSF-MULTI},
          month = {12},
          title = {A Sparsity Constraint for Topic Models - Application to Temporal Activity Mining},
      booktitle = {NIPS-2010 Workshop on Practical Applications of Sparse Modeling: Open Issues and New Directions},
           year = {2010},
       crossref = {Varadarajan_Idiap-RR-36-2010},
       abstract = {We address the mining of sequential activity patterns from document logs given as word-time occurrences. We achieve this using topics that models both the cooccurrence and the temporal order in which words occur within a temporal window. Discovering such topics, which is particularly hard when multiple activities can occur simultaneously, is conducted through the joint inference of the temporal topics and of their starting times, allowing the implicit alignment of the same activity occurences in the document. A current issue is that while we would like topic starting times to be represented by sparse distributions, this is not achieved in practice. Thus, in this paper, we propose a method that encourages sparsity, by adding regularization constraints on the searched distributions. The constraints can be used with most topic models (e.g. PLSA, LDA) and lead to a simple modified version of the EM standard optimization procedure. The effect of the sparsity constraint on our activity model and the robustness improvment in the presence of difference noises have been validated on synthetic data. Its effectiveness is also illustrated in video activity analysis, where the discovered topics capture frequent patterns that implicitly represent typical trajectories of scene objects.},
            pdf = {https://publications.idiap.ch/attachments/papers/2010/NIPS-2010, Workshop on Practical Applications of Sparse Modeling:Open Issues and New Directions_2010.pdf}
}



crossreferenced publications: 
@TECHREPORT{Varadarajan_Idiap-RR-36-2010,
         author = {Varadarajan, Jagannadan and Emonet, Remi and Odobez, Jean-Marc},
       projects = {Idiap},
          month = {10},
          title = {A Sparsity Constraint for Topic Models - Application to Temporal Activity Mining},
           type = {Idiap-RR},
         number = {Idiap-RR-36-2010},
           year = {2010},
    institution = {Idiap},
       abstract = {We address the mining of sequential activity patterns from document logs given
as word-time occurrences. We achieve this using topics that models both the cooccurrence
and the temporal order in which words occur within a temporal window.
Discovering such topics, which is particularly hard when multiple activities
can occur simultaneously, is conducted through the joint inference of the temporal
topics and of their starting times, allowing the implicit alignment of the same
activity occurences in the document. A current issue is that while we would like
topic starting times to be represented by sparse distributions, this is not achieved
in practice. Thus, in this paper, we propose a method that encourages sparsity,
by adding regularization constraints on the searched distributions. The constraints
can be used with most topic models (e.g. PLSA, LDA) and lead to a simple modified
version of the EM standard optimization procedure. The effect of the sparsity
constraint on our activity model and the robustness improvment in the presence of
difference noises have been validated on synthetic data. Its effectiveness is also
illustrated in video activity analysis, where the discovered topics capture frequent
patterns that implicitly represent typical trajectories of scene objects.},
            pdf = {https://publications.idiap.ch/attachments/reports/2010/Varadarajan_Idiap-RR-36-2010.pdf}
}