.. .. Single-manifold-learning,

. .. Numerical-experiments, 107 6.6.1 Evaluation of M3DC on real data sets

, 112 6.6.1.4 Clustering evaluation using Silhouette Score, p.117

, Evaluation of M3DC on synthetic data sets

.. .. Conclusion,

, Semi-supervised Co-clustering 123

. Constrained and . .. Cmdc),

. .. Numerical-experiments, 128 7.3.1 Evaluation of CMDC on real data sets, p.130

, 2 Evaluation of CMDC on synthetic data sets

.. .. Conclusion,

S. Co-clustering, WebACE data set : Sparsity rate= 91.83 % (b) Coil20 data set : Sparsity rate= 34.38 % (c) Leukemia data set : Sparsity rate= 0 %

, Note that, they are based on the Frobenius norm. For future work, a promising direction would be to use the I-divergence (or generalised KL-divergence). Further, we can also investigate the use of other regularization terms. For instance, we can add some sparsity constraints by using coordinate descent, ? 1,2 regularization or spectral regression, CONCLUSIONS AND PERSPECTIVES All objectives functions optimized in the thesis are reported in figure 8.1

A. , The obtained results in terms of clustering and visualization are very encouraging. However, the used strategy relies on a certain extent on the number of classes, 2015.

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