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Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05336-1
Abstract: Approximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of…
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Keywords:
streaming data;
sensitive hashing;
dimensional streaming;
high dimensional ... See more keywords
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Published in 2021 at "Neural Processing Letters"
DOI: 10.1007/s11063-021-10599-3
Abstract: We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus…
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Keywords:
high dimensional;
streaming data;
gaussian mixture;
training gaussian ... See more keywords
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Published in 2022 at "Journal of Fluid Mechanics"
DOI: 10.1017/jfm.2023.276
Abstract: Abstract Driving oscillatory flow around an obstacle generates, due to inertial rectification, a steady ‘streaming’ flow that is useful in a host of microfluidic applications. While theory has focused largely on two-dimensional flows, streaming in…
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Keywords:
three dimensional;
streaming around;
hele shaw;
dimensional streaming ... See more keywords
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Published in 2024 at "IISE Transactions"
DOI: 10.1080/24725854.2024.2443959
Abstract: Abstract Structured high-dimensional streaming data offers abundant information that is crucial for process feedback control. Nevertheless, traditional control models predominantly emphasize the global patterns of spatiotemporal correlation within responses, often neglecting the local correlation structure.…
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Keywords:
control;
high dimensional;
streaming data;
dimensional streaming ... See more keywords