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- W3046670388 abstract "Symmetric high-dimensional and sparse (SHiDS) networks are frequently found in various industrial applications. A symmetric non-negative latent factor (SNLF) model can acquire essential features from them precisely, yet it suffers from slow convergence. To address this issue, this article integrates a generalized momentum method into a symmetric, single latent factor-dependent, non-negative and multiplicative update (S <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> LF-NMU) algorithm, thereby achieving a <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>m</u> omentum-incorporated, <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>s</u> ymmetric, <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>s</u> ingle-latent-factor-dependent <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>n</u> on-negative-multiplicative-update (MS <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> N) algorithm. Based on an MS <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> N algorithm, momentum-incorporated symmetric non-negative latent factor (MSNLF) models are proposed for an SHiDS network, which ensures fast convergence as well as high representative learning ability. Empirical studies on four SHiDS networks from industrial applications demonstrate that compared with state-of-the-art models, the proposed MSNLF models have significantly higher computational efficiency and representative learning ability." @default.
- W3046670388 created "2020-08-07" @default.
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- W3046670388 date "2022-08-01" @default.
- W3046670388 modified "2023-10-11" @default.
- W3046670388 title "Momentum-Incorporated Symmetric Non-Negative Latent Factor Models" @default.
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- W3046670388 doi "https://doi.org/10.1109/tbdata.2020.3012656" @default.
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