Multiple Metric Learning for large margin kNN Classification of time series

To allow metric learning on high-dimensional image features of large scale datasets that are too large to fit in memory, we use in addition product quantization (Gray and

Our approach for time series classification builds on two well-known methods in computer vision: local features are extracted from time series using a SIFT- based approach and a

Finally, the package implements one triplet learner and one quadruplet learner: Sparse Compositional Metric Learning (SCML, Shi et al., 2014) and Metric Learning from

When dealing with high spatial resolution images, object-based approaches are generally used in order to exploit the spatial relationships of the data8. However, these

Second resubmission step: “PELICAN: deeP architecturE for the LIght Curve ANalysis” Johanna Pasquet, J´ erˆ ome Pasquet, Marc Chaumont and Dominique Fouchez. Astronomy

They include (a) traditional unsupervised clustering problems, with the classical K-means algorithm, hierarchical linkage methods [14] and spectral clustering [22], (b) unsu-

With the arrival of SITS with high spatial resolution (HSR), it becomes necessary to use the spatial information held in these series, in order to either study the evolution of

These methods aim to learn a metric that brings pairs of similar points closer together and pushes pairs of dissimilar points further away from each other.. Such supervision is