Article
Computer Science, Information Systems
Muhammad Aminu, Noor Atinah Ahmad
Summary: By incorporating a locality preserving feature, LPPLSDA enhances the performance of partial least squares discriminant analysis, especially in face recognition tasks. Experimental results consistently show that LPPLSDA outperforms the conventional PLS-DA method on various benchmarked face databases.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Biochemical Research Methods
Ruoyu Tang, Xinyu He, Ruiqi Wang
Summary: The study presents a general computational method for constructing maps between different cell fates and parametric conditions by systematic perturbations. The method does not require accurate parameter measurements or bifurcations. The maps obtained can help in understanding how systematic perturbations drive cell fate decisions and transitions, providing valuable information for predicting and controlling cell states.
Article
Mathematics
Hongmei Shi, Xingbo Zhang, Yuzhen Gao, Shuai Wang, Yufu Ning
Summary: This article introduces an uncertain total least squares estimation method and an uncertain robust total least squares linear regression method based on uncertainty theory and total least squares method. These methods can handle non-random and imprecise data and achieve more reasonable fitting effect and reliable results.
Article
Computer Science, Artificial Intelligence
Manish Narwaria, Aditya Tatu
Summary: Machine learning methods are widely used in multimedia signal processing, but they often ignore the uncertainty in ground-truth data, leading to overemphasizing single-target values. To address this issue, an uncertainty aware loss function is proposed to explicitly consider data uncertainty.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Lavi Rizki Zuhal, Ghifari Adam Faza, Pramudita Satria Palar, Rhea Patricia Liem
Summary: This paper aims to assess the potential of Kriging combined with partial least squares (KPLS) for fast uncertainty quantification and sensitivity analysis in high-dimensional problems. Results show that KPLS with four principal components is significantly faster than the ordinary Kriging while yielding comparable accuracy in approximating the statistical moments and Sobol indices.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Geosciences, Multidisciplinary
Xingxing Qiao, Chao Wang, Meichen Feng, Meijun Zhang, Xiaoyan Song, Lujie Xiao, Guangxin Li, Xiuliang Jin, Sumera Anwar, Wude Yang
Summary: Soil aggregate stability, an indicator of soil structure quality, can be efficiently estimated using hyperspectral technology. The study found a negative correlation between soil spectra and aggregate stability, with sensitive wavebands identified for each soil aggregate character. Reliable models were developed for most soil aggregate indicators, with aggregate fractal dimension proving to be a key indicator for soil aggregate stability monitoring. Hyperspectral technology offers a promising alternative method for soil aggregate assessment, particularly for large-scale applications.
Article
Optics
Yong -Mei Li, Hai -Ling Liu, Shi-Jie Pan, Su-Juan Qin, Fei Gao, Dong-Xu Sun, Qiao-Yan Wen
Summary: This paper proposes a complete quantum algorithm for the k-medoids algorithm, which utilizes quantum subroutines to improve the speed of cluster assignment and center update. Compared to existing algorithms, our quantum k-medoids algorithm achieves a polynomial speedup in large data sets.
Review
Engineering, Mechanical
Randall J. Allemang, Rohit S. Patwardhan, Murali M. Kolluri, Allyn W. Phillips
Summary: This paper outlines various FRF estimation techniques and compares algorithms that compute FRF using different methods. It also discusses inconsistencies in some conditioned coherence metrics and provides corrected interpretations.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biotechnology & Applied Microbiology
Pozzobon Victor, Cristobal Camarena-Bernard
Summary: A machine learning model was developed to quantify chlorophyll and carotenoids in algae simultaneously using spectrophotometric equations. The model was calibrated and validated, showing excellent accuracy and efficiency. The step-wise workflow presented in this study allows for easy adaptation by other researchers.
JOURNAL OF APPLIED PHYCOLOGY
(2023)
Article
Automation & Control Systems
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
Summary: This paper proposes a robust method for PLS based on the idea of least trimmed squares (LTS), which effectively deals with high-dimensional regressors. By formulating the LTS problem as a concave maximization problem, the complexity of solving LTS is simplified. The results from simulation and real data sets demonstrate the effectiveness and robustness of the proposed approach.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaokang Wang, Shan Lu, Rui Zhou, Huiwen Wang
Summary: This paper proposes a two-stage DAG skeleton estimation approach for highly correlated data. The first stage involves a novel neighborhood selection method based on sparse partial least squares regression, while the second stage estimates the DAG skeleton by evaluating conditional independence hypotheses. Simulation studies and tests on publicly available datasets demonstrate the effectiveness of the proposed method.
PATTERN RECOGNITION
(2023)
Article
Chemistry, Analytical
Jan P. M. Andries, Yvan Vander Heyden
Summary: A new PLS-DA modelling approach, CSIV-PLS1-DA, is proposed and evaluated in this study. It combines features of PLS1-DA and PLS2-DA, making it more flexible and significantly better in classification ability compared to the traditional methods.
Article
Engineering, Chemical
James Odgers, Chrysoula Kappatou, Ruth Misener, Salvador Garcia Munoz, Sarah Filippi
Summary: This article presents an approach based on bootstrapping to model the uncertainty in partial least squares (PLS), which is difficult due to the nonlinear effect of the observed data on the latent space. The approach allows for equally representing confidence intervals for points close to or far away from the latent space. Applications in determining the Design Space for industrial processes and modeling the uncertainty of spectroscopy data demonstrate the benefits and performance of the method.
Article
Engineering, Multidisciplinary
Baolei Wei
Summary: Parameter estimation is a crucial step in grey system models for time series modeling and forecasting. This study presents a separable grey system model that encompasses both linear and nonlinear models with separable structural parameters. Three least squares-based strategies are proposed for estimating structural parameters and initial conditions. Nonlinear least squares outperforms the other two strategies, especially in scenarios with large time intervals and high noise levels. Real-world applications demonstrate the effectiveness of the proposed method in forecasting failure times of products and traffic flows.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Mechanical
Yushan Liu, Luyi Li, Sihan Zhao
Summary: This paper combines Kriging and PLS to develop a global surrogate model for high-dimensional structural systems, named as PLS-K. In the proposed method, PLS is employed to identify the input-output principal components, wherein Kriging model is used to establish the relationship between each pair of principal components.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biochemical Research Methods
Werickson Fortunato de Carvalho Rocha, David A. Sheen, Daniel W. Bearden
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2018)
Article
Chemistry, Physical
Laura A. Mertens, Iftikhar A. Awan, David A. Sheen, Jeffrey A. Manion
JOURNAL OF PHYSICAL CHEMISTRY A
(2018)
Article
Instruments & Instrumentation
David A. Sheen
JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY
(2019)
Article
Energy & Fuels
Werickson Fortunado de Carvalho Rocha, David A. Sheen
Article
Multidisciplinary Sciences
Melis Onel, Burcu Beykal, Kyle Ferguson, Weihsueh A. Chiu, Thomas J. McDonald, Lan Zhou, John S. House, Fred A. Wright, David A. Sheen, Ivan Rusyn, Efstratios N. Pistikopoulos
Article
Biochemistry & Molecular Biology
Daniel W. Bearden, David A. Sheen, Yamil Simon-Manso, Bruce A. Benner, Werickson F. C. Rocha, Niksa Blonder, Katrice A. Lippa, Richard D. Beger, Laura K. Schnackenberg, Jinchun Sun, Khyati Y. Mehta, Amrita K. Cheema, Haiwei Gu, Ramesh Marupaka, G. A. Nagana Gowda, Daniel Raftery
Article
Automation & Control Systems
David A. Sheen, Vincent K. Shen, Robert G. Brinson, Luke W. Arbogast, John P. Marino, Frank Delaglio
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2020)
Article
Chemistry, Physical
Nathan A. Mahynski, Harold W. Hatch, Matthew Witman, David A. Sheen, Jeffrey R. Errington, Vincent K. Shen
Summary: By combining statistical mechanical principles with biased sampling techniques, it is possible to predict the thermodynamic properties of systems more accurately and achieve precise estimates across a wide range of conditions. These extrapolations significantly increase the amount of accurate information that can be extracted from simulations, providing data for data-intensive algorithms.
MOLECULAR SIMULATION
(2021)
Article
Biochemistry & Molecular Biology
Robert G. Brinson, K. Wade Elliott, Luke W. Arbogast, David A. Sheen, John P. Giddens, John P. Marino, Frank Delaglio
JOURNAL OF BIOMOLECULAR NMR
(2020)
Article
Energy & Fuels
Werickson Fortunato de Carvalho Rocha, Cary Presser, Shannon Bernier, Ashot Nazarian, David A. Sheen
Article
Nanoscience & Nanotechnology
Chris J. Peacock, Connor Lamont, David A. Sheen, Vincent K. Shen, Laurent Kreplak, John P. Frampton
Summary: By studying the pairwise mixing behavior of 68 water-soluble compounds and using machine learning classifiers to predict their miscibility, the random forest classifier emerged as the most successful with high levels of accuracy, specificity, and sensitivity under different scenarios. The potential of this machine learning approach to improve the design of screening experiments for aqueous two-phase systems for various scientific and industrial applications was demonstrated.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Article
Chemistry, Analytical
Evan P. Jahrman, Lee L. Yu, William P. Krekelberg, David A. Sheen, Thomas C. Allison, John L. Molloy
Summary: The speciation of arsenic plays a crucial role in its toxicity and bioavailability. This study explores the use of X-ray spectroscopies to determine arsenic speciation profiles in materials related to public health initiatives, such as food safety. The results provide insights into the efficacy of X-ray spectroscopy and the accuracy of analysis. The study also introduces the lasso regression method to improve the statistical inferences and reduce overfitting.
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2022)
Meeting Abstract
Chemistry, Multidisciplinary
David Sheen, Werickson F. C. Rocha
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
Meeting Abstract
Chemistry, Multidisciplinary
David Sheen, Bruce Benner, Yamil Simon, Werickson F. C. Rocha, Christina Jones, Niksa Blonder, Katrice Lippa
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
Article
Automation & Control Systems
David A. Sheen, Werickson F. C. Rocha, Katrice A. Lippa, Daniel W. Bearden
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2017)