The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Online kernel-based dictionary learning (DL) algorithms are considered, which perform DL on training data lifted to a high-dimensional feature space via a nonlinear mapping. Compared to batch versions, online algorithms require low computational complexity, essential for processing the Big Data, based on the stochastic gradient descent method. However, as with any kernel-based learning algorithms,...
Efficient online algorithms are developed to perform dictionary learning (DL) for the features lifted to a high-dimensional space via nonlinear mapping. Inspired by recent works on batch kernelized DL with promising performance for real-world learning tasks, two kernel DL formulations are put forth, amenable to online processing. The first formulation aims at faithfully representing the high-dimensional...
An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and background noise) scaled by attenuation functions capturing...
This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to...
Accurate imputation and prediction of load data are important prerequisites for many tasks of power systems, especially as renewables and plug-in electric vehicles penetrate the grid. A low-rank and sparse matrix factorization model is considered for load inference tasks to capture spatial as well as temporal structures in multi-site load data. The low-rank structure captures periodic patterns, and...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.