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- W4287204281 abstract "As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this paper, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that is irrelevant to the hardware parallelism, well-defined metrics for determining optimal clustering, balanced partition for maintaining the compactness property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability." @default.
- W4287204281 created "2022-07-25" @default.
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- W4287204281 date "2021-04-21" @default.
- W4287204281 modified "2023-10-11" @default.
- W4287204281 title "An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering" @default.
- W4287204281 doi "https://doi.org/10.48550/arxiv.2104.10819" @default.
- W4287204281 hasPublicationYear "2021" @default.
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