Guillaume Wacquet

Dissimilarity-Based Classification of Multidimensional Signals by Conjoint Elastic Matching: Application to Phytoplanktonic Species Recognition

The paper describes a classification method of multidimensional signals, based upon a dissimilarity measure between signals. Each new signal is compared to some reference signals through a conjoint dynamic time warping algorithm of their time features series, of which proposed cost function gives out a normalized dissimilarity degree. The classification then consists in presenting these degrees to a classifier, like k-NN, MLP or SVM. This recognition scheme is applied to the automatic estimation of the Phytoplanktonic composition of a marine sample from cytometric curves. At present, biologists are used to a manual classification of signals, that consists in a visual comparison of Phytoplanktonic profiles. The proposed method consequently provides an automatic process, as well as a similar comparison of the signal shapes. We show the relevance of the proposed dissimilarity-based classifier in this environmental application, and compare it with classifiers based on the classical DTW cost-function and also with features-based classifiers.

Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters

In this paper, we propose a new K-way semi-supervised spectral clustering method able to estimate the number of clusters automatically and then to integrate some limited supervisory information. Indeed, spectral clustering can be guided thanks to the provision of prior knowledge. For the automatic determination of the number of clusters, we propose to use a criterion based on an outlier number minimization. Then, the prior knowledge consists of pairwise constraints which indicate whether a pair of objects belongs to a same cluster (Must-Link constraints) or not (Cannot-Link constraints). The spectral clustering then aims at optimizing a cost function built as a classical Multiple Normalized Cut measure, modified in order to penalize the non-respect of these constraints. We show the relevance of the proposed method with some UCI datasets. For experiments, a comparison with other semi-supervised clustering algorithms using pairwise constraints is proposed.