Towards dynamic classification completeness in Twitter. Milioris, D. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 1098-1102, Aug, 2016.
Towards dynamic classification completeness in Twitter [pdf]Paper  doi  abstract   bibtex   
In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete matrices from a small number of random sample Joint Complexity scores. The experimental evaluation with real data from Twitter presents the topic detection accuracy based on complete reconstructed matrices, and thus reducing the exhaustive computation of Joint Complexity scores.
@InProceedings{7760418,
  author = {D. Milioris},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Towards dynamic classification completeness in Twitter},
  year = {2016},
  pages = {1098-1102},
  abstract = {In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete matrices from a small number of random sample Joint Complexity scores. The experimental evaluation with real data from Twitter presents the topic detection accuracy based on complete reconstructed matrices, and thus reducing the exhaustive computation of Joint Complexity scores.},
  keywords = {social networking (online);joint complexity;topic classification;topic detection;matrix completion;Twitter;dynamic classification completeness;Twitter;Complexity theory;Correlation;Training;Europe;Signal processing;Markov processes},
  doi = {10.1109/EUSIPCO.2016.7760418},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570251442.pdf},
}
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