In: Journal of computational science, 2021, vol. 53, p. 13
The ℓ1-regularized Gaussian maximum likelihood method is a common approach for sparse precision matrix estimation, but one that poses a computational challenge for high-dimensional datasets. We present a novel ℓ1- regularized maximum likelihood method for performant large-scale sparse precision matrix estimation utilizing the block structures in the underlying computations. We identify the...
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In: Competitiveness Review, 2016, vol. 26, no. 2, p. 188-209
Purpose – This paper aims to examine the methodology used to identify clusters on the one hand and assess the economic impact that those may have on regions on the other hand. Design/methodology/approach – The influential work on “clusters”lead by Michael Porter since the 1990s has become a tool for promoting innovation and growth at national and regional level. Even if the theory has...
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In: IEEE 37th International Conference on Data Engineering (ICDE), 2021, p. 2661--2664
Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate...
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In: Bioinformatics, 2017, vol. 33, no. 19, p. 3123-3125
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In: Bioinformatics, 2018, vol. 34, no. 8, p. 1433-1435
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In: Personal and Ubiquitous Computing, 2015, vol. 19, no. 1, p. 123-141
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In: Machine Learning, 2015, vol. 100, no. 2-3, p. 285-304
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In: Bioinformatics, 2017, vol. 33, no. 14, p. i75-i82
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In: Information Retrieval Journal, 2015, vol. 18, no. 5, p. 445-472
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In: Analytical and Bioanalytical Chemistry, 2015, vol. 407, no. 29, p. 8681-8712
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