ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
This work addresses efficient and optimal estimation in high-dimensional tensor regression, which is important for applications in fields like machine learning and data analysis, though it appears incremental as it builds on existing low-rank tensor methods.
The paper tackles the problem of low-rank tensor regression by proposing ISLET, a method based on importance sketching, which achieves minimax optimal mean-squared error under certain assumptions and offers substantial computational advantages, including handling tensors of dimension up to O(10^8) and being 1-2 orders of magnitude faster than baseline methods.
In this paper, we develop a novel procedure for low-rank tensor regression, namely \emph{\underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation for \underline{T}ensors} (ISLET). The central idea behind ISLET is \emph{importance sketching}, i.e., carefully designed sketches based on both the responses and low-dimensional structure of the parameter of interest. We show that the proposed method is sharply minimax optimal in terms of the mean-squared error under low-rank Tucker assumptions and under randomized Gaussian ensemble design. In addition, if a tensor is low-rank with group sparsity, our procedure also achieves minimax optimality. Further, we show through numerical study that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods while having substantial storage and run-time advantages including capabilities for parallel and distributed computing. In particular, our procedure performs reliable estimation with tensors of dimension $p = O(10^8)$ and is $1$ or $2$ orders of magnitude faster than baseline methods.