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- W4385834098 abstract "Motivated by applications in single-cell biology and metagenomics, we investigate the problem of matrix reordering based on a noisy disordered monotone Toeplitz matrix model. We establish the fundamental statistical limit for this problem in a decision-theoretic framework and demonstrate that a constrained least squares estimator achieves the optimal rate. However, due to its computational complexity, we analyze a popular polynomial-time algorithm, spectral seriation, and show that it is suboptimal. To address this, we propose a novel polynomial-time adaptive sorting algorithm with guaranteed performance improvement. Simulations and analyses of two real single-cell RNA sequencing datasets demonstrate the superiority of our algorithm over existing methods." @default.
- W4385834098 created "2023-08-16" @default.
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- W4385834098 date "2023-01-01" @default.
- W4385834098 modified "2023-10-16" @default.
- W4385834098 title "Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms" @default.
- W4385834098 doi "https://doi.org/10.1109/tit.2023.3305538" @default.
- W4385834098 hasPublicationYear "2023" @default.
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