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From spatial learning to machine learning: an unsupervised approach with applications to behavioral science

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VMVW02 - Generative models, parameter learning and sparsity

In this talk we consider an example-driven approach for identifying ability patterns from data partitions based on learning behaviour in the water maze experiment. A modification of the k-means algorithm for longitudinal data as introduced in [1] is used to identify clusters based on the learning variable (see [3]). The association between these clusters and the flying ability variables is statistically tested in order to characterize the partitions in terms of flying traits. Since the learning variables seem to reflect flying abilities, we propose a new sparse clustering algorithm in an approach modelling the covariance matrix by a Kronecker product. Consistency and an EM-algorithm are studied in this framework also. References: 1. Genolini, C.; Ecochard, R.; Benghezal, M. et al., ''kmlShape: An Efficient Method to Cluster Longitudinal Data (Time-Series) According to Their Shapes'', PLOS ONE ; Vol. 11, 2016.2. Sung, K.K. and Poggio, T., ''Example-based learning for view-based human face detection''; IEEE Transactions on pattern analysis and machine intelligence, Vol. 20, 39-51, 1998.; 3. Rosipal, R. and Kraemer, N. , ''Overview and recent advances in partial least squares'',  Subspace, latent structure and feature selection,  Book Series: Lecture Notes in Computer Science; Vol. 3940, 34-51, 2006.

This talk is part of the Isaac Newton Institute Seminar Series series.

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