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IPS273 Tomoki Tokuda et al.





















                  Figure 4. Classification of D1, D2 and D3. Panel (A): Distribution of subjects.
                  Panel (B): A classification tree based on Panel (A).

                  4. Conclusion
                     We  have  developed  a  novel  multiple  co-clustering  method  based  on
                  nonparametric  Bayesian  mixture  models.  This  method  can  reveal  the
                  underlying  multiple  view  structures  in  which  co-clustering  structures  with
                  different  types  of  distributions  are  imbedded.  Applying  this  method  to  a
                  concatenated  dataset  of  different  modalities  of  depression  data,  we  have
                  identified a relevant view for subtypes of depression. Further analysis of this
                  view implies a  possible classification of  treatment-resistant depression and
                  non-treatment-resistant depression prior to the onset of SSRI treatment. This
                  result is useful for further investigation of classification of MDD patients from
                  the perspective of treatment effect.

                  References
                  1.  Bishop, C.M. (2006). Pattern Recognition and Machine Learning.
                      Springer.
                  2.  Gu, Q. & Zhou, J. (2009). Co-clustering on manifolds. In: Proceedings of
                      the 15th ACM SIGKDD International Conference on Knowledge
                      Discovery and Data Mining. ACM, 359–368.
                  3.  Guan, Y., Dy, J.G., Niu, D. & Ghahramani, Z. (2010). Variational inference
                      for nonparametric multiple clustering. In: MultiClust Workshop KDD-
                      2010.
                  4.  Lazzeroni, L. & Owen, A. (2002). Plaid models for gene expression data.
                      Statistica Sinica, 12, 61–86.
                  5.  Madeira, S.C. & Oliveira, A.L. (2004). Biclustering algorithms for
                      biological data analysis: A survey. IEEE/ACM Transactions on
                      Computational Biology and Bioinformatics (TCBB), 1, 24–45.




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