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- W4287887777 abstract "High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by particle-swarm-optimization-incorporated stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>S</u> equential- <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>G</u> roup- <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>D</u> ifferential- <underline xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>E</u> volution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. The main idea of SGDE-PLFA is two-fold. First, it divides the obtained latent factors by PLFA into row and column sub-groups to ensure the efficient low-dimensional optimization. Second, it initializes and optimizes these sub-group latent factors with our improved Differential Evolution algorithm to search more accurate solutions nearby the suboptimum. As demonstrated by the extensive experiments on four HiDS matrices, an SGDE-PLFA model outperforms the state-of-the-art models in representing HiDS matrices." @default.
- W4287887777 created "2022-07-26" @default.
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- W4287887777 date "2023-04-01" @default.
- W4287887777 modified "2023-10-05" @default.
- W4287887777 title "A Differential Evolution-Enhanced Position-Transitional Approach to Latent Factor Analysis" @default.
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- W4287887777 doi "https://doi.org/10.1109/tetci.2022.3186673" @default.
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