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In this article, a new approach is proposed to study the performance of graph-based semi-supervised learning methods, under the assumptions that the dimension of data p and their number n grow large at the same rate and that the data arise from a Gaussian mixture model. Unlike small dimensional systems, the large dimensions allow for a Taylor expansion to linearize the weight (or kernel) matrix W,...
In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to realize the time integration. This particular formulation allows us to gain computationally since only relevant...
This paper analyzes the application of the Information Theoretic (IT) Mean Shift algorithm for modes finding in order to provide the classification of Electricity Customer Load Patterns. The impact of the algorithm parameters is discussed and then clustering indices are used in order to make a comparison with the classical methods available. Results show a good capability of the modes found in capturing...
This paper introduces an online receiver for multiaccess multiple-input multiple-output (MIMO) channels by using kernel functions. The receiver implicitly operates, with linear complexity, in a general infinite dimensional Reproducing Kernel Hilbert Space (RKHS).Given a training sequence of symbols, the problem is viewed as a sequential multiregression optimization problem, where any continuous convex...
This paper develops a novel, efficient, 2D, blind deconvolution algorithm for restoring images corrupted by an unknown 2D blurring kernel satisfying a separable property. The algorithm builds on known results for 2D deconvolution using the constant modulus algorithm (CMA) which is an archetype gradient descent based blind algorithm used in 1D blind deconvolution of communication systems. By exploiting...
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