Research

My research journey is described here, through some milestones along the way, older works are at the end.

Feel free to have a look at some of my publications here.

Have a nice journey !

Current research activities at LITIS Lab

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Machine Learning for Graph Data

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The Pre-image Problem in Machine Learning

Many nonlinear data processing, including Machine Learning, operate a nonlinear map of the data to some space where processing is straightforward.
The pre-image problem considers the inference of the inverse map, namely the return from the feature space to the observation space. This is a hard-to-solve ill-posed problem. Our contributions include several techniques to solve the pre-image problem, and the extension of the scope to novel applications (beyond denoising). Our work opens the way to new classes of nonlinear methods in various research areas.
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Spectral Unmixing (Blind Source Separation) in Hyperspectral Image

This part of the website is under construction.
See references in the publications page, here.

Distributed Learning for Big Data

This part of the website is under construction.
See references in the publications page, here.

Online Detection with One-Class Classification

This part of the website is under construction.
See references in the publications page, here.

Collaborative Learning in (Wireless) Sensor Networks

We propose machine learning techniques for collaborative learning in wireless sensor networks, taking into account the limited resources in computation, memory usage, communication as well as power. We are interested in wireless sensor networks deployed in an environment to sense for modeling and monitoring given physical phenomena, such as gas, pollution, biochemical, temperature, humidity, acoustic field, motion, etc. It is worth noting that collaborative learning greatly enhances the accuracy of the estimation, and the detection performance.
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GPS-Free Indoor GeoLocalization in Wireless Sensor Networks

Sensors are often randomly deployed, such as for monitoring inhospitable habitats and disaster areas, or equipping mobile entities, such as humans (e.g., Alzheimer patients in an adult daycare center) or animals in wilderness or even vehicles (in vehicular ad hoc networks (VANETs)).  Information captured by each sensor remains obsolete as long as it stays unaware of its location. Implementing a self-localization device, such as a global positioning system (GPS) receiver, at each sensor device may be too power-hungry for the desired application with battery-powered devices. Moreover, GPS or any global navigation satellite system (GNSS), are not functional in indoor environments. The inference of the position of location-unaware sensors from inter-sensor ranging is a hard and ill-posed problem. We propose many contributions, including zoning, localization and tracking, with and without radio-location fingerprints. Investigated methods include machine learning, Bayesian methods, polar intervals, pre-image problem and matrix regression.
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Online Dictionary Adaptation (Sparse Learning)

This work tackles the online identification problem for nonlinear and nonstationary systems using kernel methods. The order of the model is controlled by some sparsification criterion which leads to select the most relevant atoms to form a dictionary. We explore the dictionary adaptation using stochastic gradient descent methods along with an online kernel identification algorithm. The proposed method leads to a reduction of the instantaneous quadratic error and to a decrease in the model’s order.
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Online Learning and Nonlinear Adaptive Filtering

Kernel methods have been very useful to solve nonlinear identification problems. The main drawback of these methods resides in the fact that the number of elements of the kernel development, i.e., the size of the dictionary, increases with the number of input data, making the solution not suitable for online problems especially time series applications, adaptive filtering and online identification. Our contributions in online learning with kernel machines are given here.
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Relative Stationarity Testing

This work provides recent developments for testing stationarity of any signal relatively to an observation scale. The originality is to extract time-frequency features from a set of stationarized surrogate signals, and to use them for defining the null hypothesis of stationarity.
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Time-Frequency Learning Machines

This work extends the framework of the kernel learning machines to non-stationary signal analysis, by showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.
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