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Search · four archives
12 papers · ranked by Valyu relevance
Kaiqiong Zhao, Archer Y Yang, Karim Oualkacha, Yixiao Zeng + 5 more
Title: Summary Varying coefficient models offer the flexibility to learn the dynamic changes of regression coefficients. Despite their good interpretability and diverse applications, in high-dimensional settings, existing estimation methods for such models have important limitations. For example, we routinely encounter…
Ming Yang, Shumao Han, Linglong Chen, Jiayi Wang + 1 more
Tensor-based subspace clustering algorithms have garnered significant attention for their high efficiency in clustering high-dimensional data. However, when dealing with 2D image data, traditional vectorization operations in most algorithms tend to undermine the correlations of higher-order tensor terms. To tackle this…
Cristian Castiglione, Alexandre Segers, Lieven Clement, Davide Risso
Title: Summary Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critical for the visualization, clustering, and phenotypic…
Jin Deng, Junjie Lan, Ruolan Du, Tao Xu + 4 more
The high recurrence rate of tumor limits the growth of precision medicine, whereas the exploration of correlations in multimodal data enables mining of features linked to tumor recurrence, ultimately identifying prospective biomarkers. Nevertheless, existing multimodal approaches centered on genetic molecular data…
Hakkı Güngör
This study addresses the inverse determination of an unknown tension coefficient in a one-dimensional wave equation from final-time observations. Since coefficient reconstruction is inherently ill-posed, a quadratic Tikhonov regularization term incorporating a prior (initial guess) coefficient is employed to stabilize…
Amr Zakaria
Medical decision support requires models that remain interpretable under uncertainty while still adapting to evolving data and expert knowledge. Fuzzy cognitive maps (FCMs) are attractive in this setting because they encode concept-level relations in a transparent graphical form. However, static expert-defined FCMs are…
Changming Song, Yang Zhou, Wenguang Ji, Xuebo Zhang
We propose a new model for face recognition under insufficient sampling conditions in this paper. In the proposed method, we combine the fusion dictionary with nuclear norm regularization to preserve the details of the restored images, and adopt a Laplacian-uniform mixture function to fit the error distribution. Since…
Xin Wang, Xu Ren, Haoquan Wang
Significance Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that offers high contrast and high resolution, showing great potential for applications in medical imaging. However, existing regularization methods often lead to instability and artifacts in the reconstruction due to imbalanced…
Yuhua Fan, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann
High-dimensional genomic datasets contain complex patterns shaped by substantial biological noise, which pose major challenges for predictive modeling in genetics and breeding. Residual neural networks (ResNets) provide a powerful framework for capturing nonlinear genomic effects, but often overfit in settings where…
Clemens Kirisits, Eric Setterqvist
We prove that the $L^2$ distance between the minimizer of the $\ell ^1$-anisotropic Rudin-Osher-Fatemi (ROF) functional and its minimizer over the space of piecewise constant functions on a rectilinear grid is $\mathcal{O}h^{{1}/{2}-{q'}/{2q}}$, where h is the grid’s mesh size and the datum belongs to $L^q$, q \ge 2 q…
Mingcong Wu, Alessandro Giuliani
This paper investigates robust high-dimensional convoluted rank regression in distributed environments. We propose an estimation method suitable for sparse regimes, which remains effective under heavy-tailed errors and outliers, as it does not impose moment assumptions on the noise distribution. To facilitate scalable…
Antony Mizzi, David M. Walker, Michael Small, José F. F. Mendes
We derive a penalty strength criterion for ridge regression using stochastic complexity, which is a refined variant of the minimum description length principle. Since stochastic complexity does not typically account for the effect of regularization on complexity, despite its ability to simplify models, we are required…