12,050 research outputs found
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018
Analyzing sparse dictionaries for online learning with kernels
Many signal processing and machine learning methods share essentially the
same linear-in-the-parameter model, with as many parameters as available
samples as in kernel-based machines. Sparse approximation is essential in many
disciplines, with new challenges emerging in online learning with kernels. To
this end, several sparsity measures have been proposed in the literature to
quantify sparse dictionaries and constructing relevant ones, the most prolific
ones being the distance, the approximation, the coherence and the Babel
measures. In this paper, we analyze sparse dictionaries based on these
measures. By conducting an eigenvalue analysis, we show that these sparsity
measures share many properties, including the linear independence condition and
inducing a well-posed optimization problem. Furthermore, we prove that there
exists a quasi-isometry between the parameter (i.e., dual) space and the
dictionary's induced feature space.Comment: 10 page
Adaptive cancelation of self-generated sensory signals in a whisking robot
Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme
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