Nonlinear Filtering with Variable Bandwidth Exponential Kernels

Taseska M, Van Waterschoot T, Habets E, Talmon R (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Transactions on Signal Processing Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 68

Pages Range: 314-326

Article Number: 8932575

DOI: 10.1109/TSP.2019.2959190

Abstract

Frameworks for efficient and accurate data processing often rely on a suitable representation of measurements that capture phenomena of interest. Typically, such representations are high-dimensional vectors obtained by a transformation of raw sensor signals such as time-frequency transform, lag-map, etc. In this work, we focus on representation learning approaches that consider the measurements as the nodes of a weighted graph, with edge weights computed by a given kernel. If the kernel is chosen properly, the eigenvectors of the resulting graph affinity matrix provide suitable representation coordinates for the measurements. Consequently, tasks such as regression, classification, and filtering, can be done more efficiently than in the original domain of the data. In this paper, we address the problem of representation learning from measurements, which besides the phenomenon of interest contain undesired sources of variability. We propose data-driven kernels to learn representations that accurately parametrize the phenomenon of interest, while reducing variations due to other sources of variability. This is a non-linear filtering problem, which we approach under the assumption that certain geometric information about the undesired variables can be extracted from the measurements, e.g., using an auxiliary sensor. The applicability of the proposed kernels is demonstrated in toy problems and in a real signal processing task.

Authors with CRIS profile

Emanuël Habets Professur für wahrnehmungsbasierte räumliche Audiosignalverarbeitung (AudioLabs) (Stiftungsprofessur)

Involved external institutions

Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven BE Belgium (BE) Technion - Israel Institute of Technology IL Israel (IL)

How to cite

APA:

Taseska, M., Van Waterschoot, T., Habets, E., & Talmon, R. (2020). Nonlinear Filtering with Variable Bandwidth Exponential Kernels. IEEE Transactions on Signal Processing, 68, 314-326. https://dx.doi.org/10.1109/TSP.2019.2959190

MLA:

Taseska, Maja, et al. "Nonlinear Filtering with Variable Bandwidth Exponential Kernels." IEEE Transactions on Signal Processing 68 (2020): 314-326.

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