Multi-task learning on the edge: cost-efficiency and theoretical optimality
This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mixtures, (ii) computationally cheap and scalable. Supporting experiments on synthetic and real benchmark data demonstrate...
Main Authors: | , , |
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Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
arXiv
2021
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Subjects: | |
Online Access: | https://dx.doi.org/10.48550/arxiv.2110.04639 https://arxiv.org/abs/2110.04639 |
Summary: | This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mixtures, (ii) computationally cheap and scalable. Supporting experiments on synthetic and real benchmark data demonstrate that significant energy gains can be obtained with no performance loss. : 4 pages, 5 figures, code to reproduce figure available at: https://github.com/Sami-fak/DistributedMTLSPCA |
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