Frédéric Ratle

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We show how nonlinear embedding algorithms popular for use with <i>shallow</i> semi-supervised learning techniques such as kernel methods can be applied to deep multilayer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to <i>deep</i> learning whilst(More)
In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst(More)
A framework for semi-supervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regularizer to the loss function used for training neural networks. Training is done using stochastic gradient descent with additional balancing constraints to avoid falling into local minima. The(More)
This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to(More)
An application of machine learning algorithms to the clustering and classification of chemical data concerning heroin seizures is presented. The data concerns the chemical constituents of heroin as given by a gas chromatography analysis. Following a preprocessing step, where the six initial constituents are reduced to only two significant features, the data(More)
Heroin and cocaine gas chromatography data are analyzed using several clustering techniques. A database with clusters confirmed by police investigation is used to assess the potential of the analysis of the chemical signature of these drugs in the investigation process. Results are compared to standard methods in the field of chemical drug profiling and(More)
This paper investigates the application of novelty detection techniques to the problem of drug profiling in forensic science. Numerous one-class classifiers are tried out, from the simple k-means to the more elaborate Support Vector Data Description algorithm. The target application is the classification of illicit drugs samples as part of an existing(More)
We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization(More)
Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in chemical drug profiling, a recent field in the crime mapping community. The data has been collected using gas chromatography analysis. Several methods(More)