Relevant bibliographies by topics / Ridge leverage scores

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Ridge leverage scores'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Ridge leverage scores"

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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 (2024): e243121. http://dx.doi.org/10.1001/jamanetworkopen.2024.3121.

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ImportanceStudents who ride older school buses are often exposed to high levels of exhaust during their commutes, which may adversely affect health and school attendance. As a result, the US Environmental Protection Agency (EPA) has awarded millions of dollars to school districts to replace older, highly polluting school buses with newer, cleaner buses.ObjectiveTo leverage the EPA’s randomized allocation of funding under the 2012-2016 School Bus Rebate Programs to estimate the association between replacing old, highly polluting buses and changes in district-average standardized test scores.Des
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Vijayanand, 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 (2023): 718–34. http://dx.doi.org/10.15379/ijmst.v10i1.2634.

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This research aims to comprehensively investigate the dynamics of work-life balance and to develop predictive models using machine learning techniques to assess and predict the factors influencing work-life equilibrium. The study leverages a dataset containing 15,973 responses obtained from the global work-life survey conducted by Authentic-Happiness.com. The survey comprises 23 questions, providing a multifaceted view of how individuals manage their personal and professional lives. Initial Exploratory Data Analysis (EDA) uncovers five key dimensions: "Healthy Body," "Healthy Mind," "Expertise
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Li, Ziying, Jinnie Shin, Huan Kuang, and A. Corinne Huggins-Manley. "Exploring the Evidence to Interpret Differential Item Functioning via Response Process Data." Educational and Psychological Measurement, November 29, 2024. https://doi.org/10.1177/00131644241298975.

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Evaluating differential item functioning (DIF) in assessments plays an important role in achieving measurement fairness across different subgroups, such as gender and native language. However, relying solely on the item response scores among traditional DIF techniques poses challenges for researchers and practitioners in interpreting DIF. Recently, response process data, which carry valuable information about examinees’ response behaviors, offer an opportunity to further interpret DIF items by examining differences in response processes. This study aims to investigate the potential of response
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García-Portugués, Eduardo, and Arturo Prieto-Tirado. "Toroidal PCA via density ridges." Statistics and Computing 33, no. 5 (2023). http://dx.doi.org/10.1007/s11222-023-10273-9.

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AbstractPrincipal Component Analysis (PCA) is a well-known linear dimension-reduction technique designed for Euclidean data. In a wide spectrum of applied fields, however, it is common to observe multivariate circular data (also known as toroidal data), rendering spurious the use of PCA on it due to the periodicity of its support. This paper introduces Toroidal Ridge PCA (TR-PCA), a novel construction of PCA for bivariate circular data that leverages the concept of density ridges as a flexible first principal component analog. Two reference bivariate circular distributions, the bivariate sine
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Fu, Fengjie, Zhenegyi Cai, Sheng Jin, and Cheng Xu. "Monitoring ride‐hailing passenger security risk: An approach using human geography data." IET Intelligent Transport Systems, December 9, 2024. https://doi.org/10.1049/itr2.12601.

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AbstractRide‐hailing services pose significant security challenges for passengers, underscoring the need for effective security risk monitoring. While extensive research has addressed various aspects of ride‐hailing, few studies specifically focus on passenger security risk monitoring. This paper introduces onSecP, an online approach designed to monitor the security risks faced by ride‐hailing passengers using human geography data. onSecP comprises two phases that set it apart from conventional anomalous trajectory detection methods. First, it employs an anomalous trajectory detection model us
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Vigneswaran, G., N. Doshi, D. Maclean, et al. "Machine Learning to Predict Prostate Artery Embolization Outcomes." CardioVascular and Interventional Radiology, June 19, 2024. http://dx.doi.org/10.1007/s00270-024-03776-z.

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Abstract Purpose This study leverages pre-procedural data and machine learning (ML) techniques to predict outcomes at one year following prostate artery embolization (PAE). Materials and Methods This retrospective analysis combines data from the UK-ROPE registry and patients that underwent PAE at our institution between 2012 and 2023. Traditional ML approaches, including linear regression, lasso regression, ridge regression, decision trees and random forests, were used with leave-one-out cross-validation to predict international prostate symptom score (IPSS) at baseline and change at 1 year. P
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Dissertations / Theses on the topic "Ridge leverage scores"

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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.

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Les contributions de cette thèse se divisent en deux parties. Une première partie dédiée à l’accélération des méthodes à noyaux et une seconde à l'optimisation sous contrainte de parcimonie. Les méthodes à noyaux sont largement connues et utilisées en apprentissage automatique. Toutefois, la complexité de leur mise en œuvre est élevée et elles deviennent inutilisables lorsque le nombre de données est grand. Nous proposons dans un premier temps une approximation des Ridge Leverage Scores. Nous utilisons ensuite ces scores pour définir une distribution de probabilité pour le processus d'échantil
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Book chapters on the topic "Ridge leverage scores"

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S, Srividya M., and Anala M. R. "Machine Learning Based Framework for Human Action Detection." In Data Science and Intelligent Computing Techniques. Soft Computing Research Society, 2023. http://dx.doi.org/10.56155/978-81-955020-2-8-72.

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Understanding human actions has been an important area of computer vision based deep learning domain. Several landmark extraction frameworks like media pipe and open Pose are used to extract the landmark coordinates from the body. The proposed work leverages open-source body landmark extraction and then trains a deep learning model on custom dataset created. The proposed work classifies the human body actions into blank face, yawn, namaste, punch and kick actions. The dataset creation phase involved recording of actions corresponding to every class and flattening them into a data frame. The da
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Conference papers on the topic "Ridge leverage scores"

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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.

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Cohen, 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. Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974782.115.

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Zhou, Caroline, and Ivan Revilla. "A Novel Framework for Monitoring Parkinson's Disease Progression through Video Analysis and Machine Learning." In 11th International Conference on Computer Science, Engineering and Information Technology. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141421.

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Parkinson's disease (PD) is a progressive neurological disorder that necessitates continuous and accurate monitoring for effective management [4]. We propose an innovative system that leverages video analysis and machine learning to predict clinical scores for PD patients. Our system includes a mobile application for recording and uploading videos, a cloud-based server for processing the data, and a machine learning model for analyzing the videos [5]. Key technologies employed include Flutter for the mobile app, Firebase for data storage and authentication, and advanced machine learning models
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Baral, Gaurab, and Junxiu Zhou. "A hybrid Regression method for Predicting Housing Prices." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005725.

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Accurate house price prediction is crucial for accommodating the diverse needs of stakeholders in the home-buying process. House prices can be affected by various factors, such as location, construction date, exterior, etc. This work proposes a hybrid regression method that leverages the strengths of different regression techniques to improve prediction accuracy. Specifically, this work looks at conventional linear regression and other machine learning techniques such as support vector regression (SVR), and XGBoost regression. Then we compare these models with our proposed hybrid regression mo
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