Academic literature on the topic 'Ridge leverage scores'
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Journal articles on the topic "Ridge leverage scores"
Pedde, Meredith, Adam Szpiro, Richard A. Hirth, and Sara D. Adar. "School Bus Rebate Program and Student Educational Performance Test Scores." JAMA Network Open 7, no. 3 (March 20, 2024): e243121. http://dx.doi.org/10.1001/jamanetworkopen.2024.3121.
Full textVijayanand, Deepshika, and Subbulakshmi P. "Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance." International Journal of Membrane Science and Technology 10, no. 1 (October 11, 2023): 718–34. http://dx.doi.org/10.15379/ijmst.v10i1.2634.
Full textGarcía-Portugués, Eduardo, and Arturo Prieto-Tirado. "Toroidal PCA via density ridges." Statistics and Computing 33, no. 5 (July 24, 2023). http://dx.doi.org/10.1007/s11222-023-10273-9.
Full textDissertations / Theses on the topic "Ridge leverage scores"
Cherfaoui, Farah. "Echantillonnage pour l'accélération des méthodes à noyaux et sélection gloutonne pour les représentations parcimonieuses." Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0256.
Full textThe contributions of this thesis are divided into two parts. The first part is dedicated to the acceleration of kernel methods and the second to optimization under sparsity constraints. Kernel methods are widely known and used in machine learning. However, the complexity of their implementation is high and they become unusable when the number of data is large. We first propose an approximation of Ridge leverage scores. We then use these scores to define a probability distribution for the sampling process of the Nyström method in order to speed up the kernel methods. We then propose a new kernel-based framework for representing and comparing discrete probability distributions. We then exploit the link between our framework and the maximum mean discrepancy to propose an accurate and fast approximation of the latter. The second part of this thesis is devoted to optimization with sparsity constraint for signal optimization and random forest pruning. First, we prove under certain conditions on the coherence of the dictionary, the reconstruction and convergence properties of the Frank-Wolfe algorithm. Then, we use the OMP algorithm to reduce the size of random forests and thus reduce the size needed for its storage. The pruned forest consists of a subset of trees from the initial forest selected and weighted by OMP in order to minimize its empirical prediction error
Book chapters on the topic "Ridge leverage scores"
S, Srividya M., and Anala M. R. "Machine Learning Based Framework for Human Action Detection." In Data Science and Intelligent Computing Techniques, 849–57. Soft Computing Research Society, 2023. http://dx.doi.org/10.56155/978-81-955020-2-8-72.
Full textConference papers on the topic "Ridge leverage scores"
Cherfaoui, Farah, Hachem Kadri, and Liva Ralaivola. "Scalable Ridge Leverage Score Sampling for the Nyström Method." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747039.
Full textCohen, Michael B., Cameron Musco, and Christopher Musco. "Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling." In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974782.115.
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