The Experts below are selected from a list of 5148 Experts worldwide ranked by ideXlab platform
Angshul Majumdar - One of the best experts on this subject based on the ideXlab platform.
-
nuclear norm regularized robust dictionary learning for energy disaggregation
European Signal Processing Conference, 2016Co-Authors: Megha Gupta, Angshul MajumdarAbstract:The goal of this work is energy disaggregation. A recent work showed that instead of employing the Usual Euclidean norm cost function for dictionary learning, better results can be achieved by learning the dictionaries in a robust fashion by employing an l 1 -norm cost function; this is because energy data is corrupted by large but sparse outliers. In this work we propose to improve the robust dictionary learning approach by imposing low-rank penalty on the learned coefficients. The ensuing formulation is solved using a combination of Split Bregman and Majorization Minimization approach. Experiments on the REDD dataset reveal that our proposed method yields better results than both the robust dictionary learning technique and the recently published work on powerlet energy disaggregation.
-
EUSIPCO - Nuclear norm regularized robust dictionary learning for energy disaggregation
2016 24th European Signal Processing Conference (EUSIPCO), 2016Co-Authors: Megha Gupta, Angshul MajumdarAbstract:The goal of this work is energy disaggregation. A recent work showed that instead of employing the Usual Euclidean norm cost function for dictionary learning, better results can be achieved by learning the dictionaries in a robust fashion by employing an l 1 -norm cost function; this is because energy data is corrupted by large but sparse outliers. In this work we propose to improve the robust dictionary learning approach by imposing low-rank penalty on the learned coefficients. The ensuing formulation is solved using a combination of Split Bregman and Majorization Minimization approach. Experiments on the REDD dataset reveal that our proposed method yields better results than both the robust dictionary learning technique and the recently published work on powerlet energy disaggregation.
Megha Gupta - One of the best experts on this subject based on the ideXlab platform.
-
nuclear norm regularized robust dictionary learning for energy disaggregation
European Signal Processing Conference, 2016Co-Authors: Megha Gupta, Angshul MajumdarAbstract:The goal of this work is energy disaggregation. A recent work showed that instead of employing the Usual Euclidean norm cost function for dictionary learning, better results can be achieved by learning the dictionaries in a robust fashion by employing an l 1 -norm cost function; this is because energy data is corrupted by large but sparse outliers. In this work we propose to improve the robust dictionary learning approach by imposing low-rank penalty on the learned coefficients. The ensuing formulation is solved using a combination of Split Bregman and Majorization Minimization approach. Experiments on the REDD dataset reveal that our proposed method yields better results than both the robust dictionary learning technique and the recently published work on powerlet energy disaggregation.
-
EUSIPCO - Nuclear norm regularized robust dictionary learning for energy disaggregation
2016 24th European Signal Processing Conference (EUSIPCO), 2016Co-Authors: Megha Gupta, Angshul MajumdarAbstract:The goal of this work is energy disaggregation. A recent work showed that instead of employing the Usual Euclidean norm cost function for dictionary learning, better results can be achieved by learning the dictionaries in a robust fashion by employing an l 1 -norm cost function; this is because energy data is corrupted by large but sparse outliers. In this work we propose to improve the robust dictionary learning approach by imposing low-rank penalty on the learned coefficients. The ensuing formulation is solved using a combination of Split Bregman and Majorization Minimization approach. Experiments on the REDD dataset reveal that our proposed method yields better results than both the robust dictionary learning technique and the recently published work on powerlet energy disaggregation.
Steven S. Plotkin - One of the best experts on this subject based on the ideXlab platform.
-
Structural alignment using the generalized Euclidean distance between conformations
International Journal of Quantum Chemistry, 2009Co-Authors: Ali R. Mohazab, Steven S. PlotkinAbstract:The Usual Euclidean distance may be generalized to extended objects such as polymers or membranes. Here, this distance is used for the first time as a cost function to align structures. We examined the alignment of extended strands to idealized beta-hairpins of various sizes using several cost functions, including RMSD, MRSD, and the minimal distance. We find that using minimal distance as a cost function typically results in an aligned structure that is globally different than that given by an RMSD-based alignment. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem 109: 3217-3228, 2009
-
Minimal Folding Pathways for Coarse-Grained Biopolymer Fragments
Biophysical journal, 2008Co-Authors: Ali R. Mohazab, Steven S. PlotkinAbstract:The minimal folding pathway or trajectory for a biopolymer can be defined as the transformation that minimizes the total distance traveled between a folded and an unfolded structure. This involves generalizing the Usual Euclidean distance from points to one-dimensional objects such as a polymer. We apply this distance here to find minimal folding pathways for several candidate protein fragments, including the helix, the β-hairpin, and a nonplanar structure where chain noncrossing is important. Comparing the distances traveled with root mean-squared distance and mean root-squared distance, we show that chain noncrossing can have large effects on the kinetic proximity of apparently similar conformations. Structures that are aligned to the β-hairpin by minimizing mean root-squared distance, a quantity that closely approximates the true distance for long chains, show globally different orientation than structures aligned by minimizing root mean-squared distance.
Richard L. Wheeden - One of the best experts on this subject based on the ideXlab platform.
-
Self-Improving Properties of John-Nirenberg and Poincare Inequalities on Spaces of Homogeneous Type
Journal of Functional Analysis, 1998Co-Authors: Bruno Franchi, Carlos Pérez, Richard L. WheedenAbstract:Abstract We give a condition which ensures that if one inequality of Sobolev–Poincare type is valid then other stronger inequalities of a similar type also hold, including weighted versions. Our main result includes many previously known results as special cases. We carry out the analysis in the context of spaces of homogeneous type, but the main result is new even in the Usual Euclidean setting.
Jérémie Bigot - One of the best experts on this subject based on the ideXlab platform.
-
Frechet means of curves for signal averaging and application to ECG data analysis
The Annals of Applied Statistics, 2013Co-Authors: Jérémie BigotAbstract:Signal averaging is the process that consists in computing a mean shape from a set of noisy signals. In the presence of geometric variability in time in the data, the Usual Euclidean mean of the raw data yields a mean pattern that does not reflect the typical shape of the observed signals. In this setting, it is necessary to use alignment techniques for a precise synchronization of the signals, and then to average the aligned data to obtain a consistent mean shape. In this paper, we study the numerical performances of Frechet means of curves which are extensions of the Usual Euclidean mean to spaces endowed with non-Euclidean metrics. This yields a new algorithm for signal averaging and for the estimation of the time variability of a set of signals. We apply this approach to the analysis of heartbeats from ECG records.
-
Fr\'echet means of curves for signal averaging and application to ECG data analysis
arXiv: Applications, 2011Co-Authors: Jérémie BigotAbstract:Signal averaging is the process that consists in computing a mean shape from a set of noisy signals. In the presence of geometric variability in time in the data, the Usual Euclidean mean of the raw data yields a mean pattern that does not reflect the typical shape of the observed signals. In this setting, it is necessary to use alignment techniques for a precise synchronization of the signals, and then to average the aligned data to obtain a consistent mean shape. In this paper, we study the numerical performances of Fr\'echet means of curves which are extensions of the Usual Euclidean mean to spaces endowed with non-Euclidean metrics. This yields a new algorithm for signal averaging without a reference template. We apply this approach to the estimation of a mean heart cycle from ECG records.
-
Fréchet means of curves for signal averaging and application to ECG data analysis
2011Co-Authors: Jérémie BigotAbstract:Signal averaging is the process that consists in computing a mean shape from a set of noisy signals. In the presence of geometric variability in time in the data, the Usual Euclidean mean of the raw data yields a mean pattern that does not reflect the typical shape of the observed signals. In this setting, it is necessary to use alignment techniques for a precise synchronization of the signals, and then to average the aligned data to obtain a consistent mean shape. In this paper, we study the numerical performances of Fréchet means of curves which are extensions of the Usual Euclidean mean to spaces endowed with non-Euclidean metrics. This yields a new algorithm for signal averaging without a reference template. We apply this approach to the estimation of a mean heart cycle from ECG records.
