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arXiv · 2412.18964

Tensor Density Estimator by Convolution-Deconvolution

Yifan Peng·Siyao Yang·Yuehaw Khoo·Daren Wang
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Abstract

We propose a linear algebraic framework for performing density estimation. It consists of three simple steps: convolving the empirical distribution with certain smoothing kernels to remove the exponentially large variance; compressing the empirical distribution after convolution as a tensor train, with efficient tensor decomposition algorithms; and finally, applying a deconvolution step to recover the estimated density from such tensor-train representation. Numerical results demonstrate the high accuracy and efficiency of the proposed methods.

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Yifan Peng, Siyao Yang, Yuehaw Khoo, Daren Wang. 2024-12-25. Tensor Density Estimator by Convolution-Deconvolution. https://arxiv.org/abs/2412.18964

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