Feb. 9, 2024, 5:44 a.m. | Chao Chen Tian Zhou Yanjun Zhao Hui Liu Liang Sun Rong Jin

cs.LG updates on arXiv.org arxiv.org

Spatio-temporal forecasting, pivotal in numerous fields, hinges on the delicate equilibrium between isolating nuanced patterns and sifting out noise. To tackle this, we introduce Sparse Regression-based Vector Quantization (SVQ), a novel technique that leverages sparse regression for succinct representation, an approach theoretically and practically favored over classical clustering-based vector quantization methods. This approach preserves critical details from the original vectors using a regression model while filtering out noise via sparse design. Moreover, we approximate the sparse regression process using a …

clustering cs.cv cs.lg equilibrium fields forecasting noise novel patterns pivotal quantization regression representation temporal vector

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