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Academic literature on the topic 'Recursive partial least squares'

Author: Grafiati

Published: 3 June 2025

Last updated: 22 June 2025

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Journal articles on the topic "Recursive partial least squares"

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Helland, Kristian, Hans E. Berntsen, Odd S. Borgen, and Harald Martens. "Recursive algorithm for partial least squares regression." Chemometrics and Intelligent Laboratory Systems 14, no. 1-3 (1992): 129–37. http://dx.doi.org/10.1016/0169-7439(92)80098-o.

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Merino, A., D. Garcia-Alvarez, G. I. Sainz-Palmero, L. F. Acebes, and M. J. Fuente. "Knowledge based recursive non-linear partial least squares (RNPLS)." ISA Transactions 100 (May 2020): 481–94. http://dx.doi.org/10.1016/j.isatra.2020.01.006.

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Eliseyev, Andrey, and Tetiana Aksenova. "Recursive N-Way Partial Least Squares for Brain-Computer Interface." PLoS ONE 8, no. 7 (2013): e69962. http://dx.doi.org/10.1371/journal.pone.0069962.

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Wang, Xun, Uwe Kruger, and Barry Lennox. "Recursive partial least squares algorithms for monitoring complex industrial processes." Control Engineering Practice 11, no. 6 (2003): 613–32. http://dx.doi.org/10.1016/s0967-0661(02)00096-5.

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Ni, Wangdong, Soon Keat Tan, Wun Jern Ng, and Steven D. Brown. "Localized, Adaptive Recursive Partial Least Squares Regression for Dynamic System Modeling." Industrial & Engineering Chemistry Research 51, no. 23 (2012): 8025–39. http://dx.doi.org/10.1021/ie203043q.

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Vahidpour, Vahid, Amir Rastegarnia, Azam Khalili, and Saeid Sanei. "Analysis of partial diffusion recursive least squares adaptation over noisy links." IET Signal Processing 11, no. 6 (2017): 749–57. http://dx.doi.org/10.1049/iet-spr.2016.0544.

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Arablouei, Reza, Kutluyil Dogancay, Stefan Werner, and Yih-Fang Huang. "Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion." IEEE Transactions on Signal Processing 62, no. 14 (2014): 3510–22. http://dx.doi.org/10.1109/tsp.2014.2327005.

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Vijaysai, P., R. D. Gudi, and S. Lakshminarayanan. "Identification on Demand Using a Blockwise Recursive Partial Least-Squares Technique†." Industrial & Engineering Chemistry Research 42, no. 3 (2003): 540–54. http://dx.doi.org/10.1021/ie020042r.

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Poerio, Dominic V., and Steven D. Brown. "A frequency-localized recursive partial least squares ensemble for soft sensing." Journal of Chemometrics 32, no. 5 (2018): e2999. http://dx.doi.org/10.1002/cem.2999.

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Bayrak, Elif Seyma, Kamuran Turksoy, Ali Cinar, Lauretta Quinn, Elizabeth Littlejohn, and Derrick Rollins. "Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models." Journal of Diabetes Science and Technology 7, no. 1 (2013): 206–14. http://dx.doi.org/10.1177/193229681300700126.

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Dissertations / Theses on the topic "Recursive partial least squares"

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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.

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Hassel, Per Anker. "Nonlinear partial least squares." Thesis, University of Newcastle Upon Tyne, 2003. http://hdl.handle.net/10443/465.

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Partial Least Squares (PLS) has been shown to be a versatile regression technique with an increasing number of applications in the areas of process control, process monitoring and process analysis. This Thesis considers the area of nonlinear PLS; a nonlinear projection based regression technique. The nonlinearity is introduced as a univariate nonlinear function between projections, or to be more specific, linear combinations of the predictor and the response variables. As for the linear case, the method should handle multicollinearity, underdetermined and noisy systems. Although linear PLS is accepted as an empirical regression method, none of the published nonlinear PLS algorithms have achieved widespread acceptance. This is confirmed from a literature survey where few real applications of the methodology were found. This Thesis investigates two nonlinear PLS methodologies, in particular focusing on their limitations. Based on these studies, two nonlinear PLS algorithms are proposed. In the first of the two existing approaches investigated, the projections are updated by applying an optimization method to reduce the error of the nonlinear inner mapping. This ensures that the error introduced by the nonlinear inner mapping is minimized. However, the procedure is limited as a consequence of problems with the nonlinear optimisation. A new algorithm, Nested PLS (NPLS), is developed to address these issues. In particular, a separate inner PLS is used to update the projections. The NPLS algorithm is shown to outperform existing algorithms for a wide range of regression problems and has the potential to become a more widely accepted nonlinear PLS algorithm than those currently reported in the literature. In the second of the existing approaches, the projections are identified by examining each variable independently, as opposed to minimizing the error of the nonlinear inner mapping directly. Although the approach does not necessary identify the underlying functional relationship, the problems of overfitting and other problems associated with optimization are reduced. Since the underlying functional relationship may not be established accurately, the reliability of the nonlinear inner mapping will be reduced. To address this problem a new algorithm, the Reciprocal Variance PLS (RVPLS), is proposed. Compared with established methodology, RVPLS focus more on finding the underlying structure, thus reducing the difficulty of finding an appropriate inner mapping. RVPLS is shown to perform well for a number of applications, but does not have the wide-ranging performance of Nested PLS.

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Bian, Xiaomeng. "Completely Recursive Least Squares and Its Applications." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1518.

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The matrix-inversion-lemma based recursive least squares (RLS) approach is of a recursive form and free of matrix inversion, and has excellent performance regarding computation and memory in solving the classic least-squares (LS) problem. It is important to generalize RLS for generalized LS (GLS) problem. It is also of value to develop an efficient initialization for any RLS algorithm. In Chapter 2, we develop a unified RLS procedure to solve the unconstrained/linear-equality (LE) constrained GLS. We also show that the LE constraint is in essence a set of special error-free observations and further consider the GLS with implicit LE constraint in observations (ILE-constrained GLS). Chapter 3 treats the RLS initialization-related issues, including rank check, a convenient method to compute the involved matrix inverse/pseudoinverse, and resolution of underdetermined systems. Based on auxiliary-observations, the RLS recursion can start from the first real observation and possible LE constraints are also imposed recursively. The rank of the system is checked implicitly. If the rank is deficient, a set of refined non-redundant observations is determined alternatively. In Chapter 4, base on [Li07], we show that the linear minimum mean square error (LMMSE) estimator, as well as the optimal Kalman filter (KF) considering various correlations, can be calculated from solving an equivalent GLS using the unified RLS. In Chapters 5 & 6, an approach of joint state-and-parameter estimation (JSPE) in power system monitored by synchrophasors is adopted, where the original nonlinear parameter problem is reformulated as two loosely-coupled linear subproblems: state tracking and parameter tracking. Chapter 5 deals with the state tracking which determines the voltages in JSPE, where dynamic behavior of voltages under possible abrupt changes is studied. Chapter 6 focuses on the subproblem of parameter tracking in JSPE, where a new prediction model for parameters with moving means is introduced. Adaptive filters are developed for the above two subproblems, respectively, and both filters are based on the optimal KF accounting for various correlations. Simulations indicate that the proposed approach yields accurate parameter estimates and improves the accuracy of the state estimation, compared with existing methods.

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RENTERIA, RAUL PIERRE. "ALGORITHMS FOR PARTIAL LEAST SQUARES REGRESSION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=4362@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Muitos problemas da área de aprendizagem automática tem por objetivo modelar a complexa relação existente num sisitema , entre variáveis de entrada X e de saída Y na ausência de um modelo teórico. A regressão por mínimos quadrados parciais PLS ( Partial Least Squares) constitui um método linear para resolução deste tipo de problema , voltado para o caso de um grande número de variáveis de entrada quando comparado com número de amostras. Nesta tese , apresentamos uma variante do algoritmo clássico PLS para o tratamento de grandes conjuntos de dados , mantendo um bom poder preditivo. Dentre os principais resultados destacamos um versão paralela PPLS (Parallel PLS ) exata para o caso de apenas um variável de saída e um versão rápida e aproximada DPLS (DIRECT PLS) para o caso de mais de uma variável de saída. Por outro lado ,apresentamos também variantes para o aumento da qualidade de predição graças à formulação não linear. São elas o LPLS ( Lifted PLS ), algoritmo para o caso de apenas uma variável de saída, baseado na teoria de funções de núcleo ( kernel functions ), uma formulação kernel para o DPLS e um algoritmo multi-kernel MKPLS capaz de uma modelagemmais compacta e maior poder preditivo, graças ao uso de vários núcleos na geração do modelo.<br>The purpose of many problems in the machine learning field isto model the complex relationship in a system between the input X and output Y variables when no theoretical model is available. The Partial Least Squares (PLS)is one linear method for this kind of problem, for the case of many input variables when compared to the number of samples. In this thesis we present versions of the classical PLS algorithm designed for large data sets while keeping a good predictive power. Among the main results we highlight PPLS (Parallel PLS), a parallel version for the case of only one output variable, and DPLS ( Direct PLS), a fast and approximate version, for the case fo more than one output variable. On the other hand, we also present some variants of the regression algorithm that can enhance the predictive quality based on a non -linear formulation. We indroduce LPLS (Lifted PLS), for the case of only one dependent variable based on the theory of kernel functions, KDPLS, a non-linear formulation for DPLS, and MKPLS, a multi-kernel algorithm that can result in a more compact model and a better prediction quality, thankas to the use of several kernels for the model bulding.

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Hutchinson, Derek Charles Glenn. "Manipulator inverse kinematics based on recursive least squares estimation." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/27890.

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The inverse kinematics problem for six degree of freedom robots having a separable structure with the wrist equivalent to a spherical joint is considered and an iterative solution based on estimating the inverse Jacobian by recursive least squares estimation is proposed. This solution is found to have properties similar to Wampler's Damped Least Squares method and provides a stable result when the manipulator is in singular regions. Furthermore, the solution is more computationally efficient than Wampler's method; however, its best performance is obtained when the distances between the current end effector pose and the target pose are small. No knowledge of the manipulator's geometry is required provided that the end effector and joint position data are obtained from sensor information. This permits the algorithm to be readily transferable among manipulators and circumvents detailed analysis of the manipulator's structure.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate

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Walke, Richard Lewis. "High sample-rate Givens rotations for recursive least squares." Thesis, University of Warwick, 1997. http://wrap.warwick.ac.uk/36283/.

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The design of an application-specific integrated circuit of a parallel array processor is considered for recursive least squares by QR decomposition using Givens rotations, applicable in adaptive filtering and beamforming applications. Emphasis is on high sample-rate operation, which, for this recursive algorithm, means that the time to perform arithmetic operations is critical. The algorithm, architecture and arithmetic are considered in a single integrated design procedure to achieve optimum results. A realisation approach using standard arithmetic operators, add, multiply and divide is adopted. The design of high-throughput operators with low delay is addressed for fixed- and floating-point number formats, and the application of redundant arithmetic considered. New redundant multiplier architectures are presented enabling reductions in area of up to 25%, whilst maintaining low delay. A technique is presented enabling the use of a conventional tree multiplier in recursive applications, allowing savings in area and delay. Two new divider architectures are presented showing benefits compared with the radix-2 modified SRT algorithm. Givens rotation algorithms are examined to determine their suitability for VLSI implementation. A novel algorithm, based on the Squared Givens Rotation (SGR) algorithm, is developed enabling the sample-rate to be increased by a factor of approximately 6 and offering area reductions up to a factor of 2 over previous approaches. An estimated sample-rate of 136 MHz could be achieved using a standard cell approach and O.35pm CMOS technology. The enhanced SGR algorithm has been compared with a CORDIC approach and shown to benefit by a factor of 3 in area and over 11 in sample-rate. When compared with a recent implementation on a parallel array of general purpose (GP) DSP chips, it is estimated that a single application specific chip could offer up to 1,500 times the computation obtained from a single OP DSP chip.

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Tsakiris, Manolis. "On the regularization of the recursive least squares algorithm." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-21102010-101424/.

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This thesis is concerned with the issue of the regularization of the Recursive Least-Squares (RLS) algorithm. In the first part of the thesis, a novel regularized exponentially weighted array RLS algorithm is developed, which circumvents the problem of fading regularization that is inherent to the standard regularized exponentially weighted RLS formulation, while allowing the employment of generic time-varying regularization matrices. The standard equations are directly perturbed via a chosen regularization matrix; then the resulting recursions are extended to the array form. The price paid is an increase in computational complexity, which becomes cubic. The superiority of the algorithm with respect to alternative algorithms is demonstrated via simulations in the context of adaptive beamforming, in which low filter orders are employed, so that complexity is not an issue. In the second part of the thesis, an alternative criterion is motivated and proposed for the dynamical regulation of regularization in the context of the standard RLS algorithm. The regularization is implicitely achieved via dithering of the input signal. The proposed criterion is of general applicability and aims at achieving a balance between the accuracy of the numerical solution of a perturbed linear system of equations and its distance from the analytical solution of the original system, for a given computational precision. Simulations show that the proposed criterion can be effectively used for the compensation of large condition numbers, small finite precisions and unecessary large values of the regularization.<br>Esta tese trata da regularização do algoritmo dos mínimos-quadrados recursivo (Recursive Least-Squares - RLS). Na primeira parte do trabalho, um novo algoritmo array com matriz de regularização genérica e com ponderação dos dados exponencialmente decrescente no tempo é apresentado. O algoritmo é regularizado via perturbação direta da inversa da matriz de auto-correlação (Pi) por uma matriz genérica. Posteriormente, as equações recursivas são colocadas na forma array através de transformações unitárias. O preço a ser pago é o aumento na complexidade computacional, que passa a ser de ordem cúbica. A robustez do algoritmo resultante ´e demonstrada via simula¸coes quando comparado com algoritmos alternativos existentes na literatura no contexto de beamforming adaptativo, no qual geralmente filtros com ordem pequena sao empregados, e complexidade computacional deixa de ser fator relevante. Na segunda parte do trabalho, um critério alternativo ´e motivado e proposto para ajuste dinâmico da regularização do algoritmo RLS convencional. A regularização é implementada pela adição de ruído branco no sinal de entrada (dithering), cuja variância é controlada por um algoritmo simples que explora o critério proposto. O novo critério pode ser aplicado a diversas situações; procura-se alcançar um balanço entre a precisão numérica da solução de um sistema linear de equações perturbado e sua distância da solução do sistema original não-perturbado, para uma dada precisão. As simulações mostram que tal critério pode ser efetivamente empregado para compensação de números de condicionamento (CN) elevados, baixa precisão numérica, bem como valores de regularização excessivamente elevados.

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Rogers, C. A. "Partial least squares (PLS) : a comparative assessment." Thesis, University of Bath, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235583.

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Lightbody, Gaye. "High performance VLSI architectures for recursive least squares adaptive filtering." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313974.

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Lannsjö, Fredrik. "Forecasting the Business Cycle using Partial Least Squares." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378.

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Partial Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly correlated) variables. While being a well established modeling tool in chemometrics, this thesis adapts PLS to financial data to predict the movements of the business cycle represented by the OECD Composite Leading Indicators. High-dimensional data is used, and a model with automated variable selection through a genetic algorithm is developed to forecast different economic regions with good results in out-of-sample tests.<br>Partial Least Squares är både en regressionsmetod och ett verktyg för variabelselektion som är specielltlämpligt för modeller baserade på en stor mängd (möjligtvis korrelerade) variabler.Medan det är en väletablerad modelleringsmetod inom kemimetri, anpassar den häruppsatsen PLS till finansiell data för att förutspå rörelserna av konjunkturen,representerad av OECD's Composite Leading Indicator. Högdimensionella dataanvänds och en model med automatiserad variabelselektion via en genetiskalgoritm utvecklas för att göra en prognos av olika ekonomiska regioner medgoda resultat i out-of-sample-tester

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Books on the topic "Recursive partial least squares"

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Latan, Hengky, and Richard Noonan, eds. Partial Least Squares Path Modeling. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64069-3.

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Esposito Vinzi, Vincenzo, Wynne W. Chin, Jörg Henseler, and Huiwen Wang, eds. Handbook of Partial Least Squares. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-32827-8.

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Latan, Hengky, Joseph F. Hair,, and Richard Noonan, eds. Partial Least Squares Path Modeling. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37772-3.

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United States. National Aeronautics and Space Administration., ed. On recursive least-squares filtering algorithms and implementations. University of California, 1990.

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United States. National Aeronautics and Space Administration., ed. On recursive least-squares filtering algorithms and implementations. University of California, 1990.

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Avkiran, Necmi K., and Christian M. Ringle, eds. Partial Least Squares Structural Equation Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71691-6.

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Olszanskyj, Serge. Rank-k modification for recursive least squares problems. Cornell Theory Center, Cornell University, 1993.

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Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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Walke, Richard Lewis. High sample-rate Givens rotations for recursive least squares. typescript, 1997.

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Bochev, Pavel B. Least-squares finite element methods. Springer, 2009.

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Book chapters on the topic "Recursive partial least squares"

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Li, Bo, Chongshi Gu, Zhilu Li, and Lili Liu. "Application of Weighted Block Recursive Partial Least Squares Regression for Dam Safety Monitoring." In Advances in Water Resources and Hydraulic Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89465-0_316.

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Cajilima, Wilson, and Pablo Arévalo. "Multi-Method Spectral Predictive Models with FTIR-ATR in the Simultaneous Quantification of Ethanol and Legal Methanol Limits in Ecuadorian Clear Spirits." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_7.

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Abstract In this research, the feasibility of employing multiple machine learning algorithms with FTIR-ATR spectroscopy for the simultaneous quantification of ethanol and legal limits of methanol in distilled and artisanal beverages characteristic of Ecuador is investigated, based on spectral matrix similarity. Initially, spectra acquired in the range of 4000 to 400 $${\text{cm}}^{-1}$$ cm - 1 underwent spectral preprocessing including baseline correction, smoothing, normalization, first and second derivative, and their combinations. Forty-eight distinct treatments were used to construct models employing Principal Component Regression (PCR) and Partial Least Squares 2 (PLS2). The treatment yielding superior metrics was employed for constructing an Artificial Neural Network combined with Principal Component Analysis (PCA-ANN) and Recursive Feature Elimination (RFE-ANN) utilizing a Decision Tree Regressor as a variable selector. Based on confidence intervals and hypothesis testing of statistics such as root mean squared error of prediction (RMSEP) the PCR and PLS2 models exhibited superior performance. PCR achieved detection and quantification limits of 0.25% and 0.7%, respectively. In commercial beverages, predictions were compared with results obtained via gas chromatography, where again PCR and PLS2 demonstrated the finest metrics, showcasing speed and high cost-effectiveness, rendering them viables alternatives for preliminary quality control analysis.

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Chiang, Leo H., Evan L. Russell, and Richard D. Braatz. "Partial Least Squares." In Fault Detection and Diagnosis in Industrial Systems. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0347-9_6.

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Russell, Evan L., Leo H. Chiang, and Richard D. Braatz. "Partial Least Squares." In Advances in Industrial Control. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0409-4_6.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Partial Least Squares." In Machine Learning in Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5824-7_16.

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Benesty, Jacob, Constantin Paleologu, Tomas Gänsler, and Silviu Ciochină. "Recursive Least-Squares Algorithms." In A Perspective on Stereophonic Acoustic Echo Cancellation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22574-1_6.

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Young, Peter C. "Recursive Least Squares Estimation." In Recursive Estimation and Time-Series Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21981-8_3.

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Cook, R. Dennis, and Liliana Forzani. "Partial PLS and Partial Envelopes." In Partial Least Squares Regression. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003482475-6.

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Abdi, Hervé, and Lynne J. Williams. "Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-059-5_23.

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Strobach, Peter. "Recursive Least-Squares Transversal Algorithms." In Springer Series in Information Sciences. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75206-3_5.

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Conference papers on the topic "Recursive partial least squares"

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Cai, Yongjie, Dongliang Fu, Jiongmin Yu, and Wei Gao. "Stochastic Analysis of Regularized Recursive Least-Squares Algorithm." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846628.

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Li, Fengyawen. "Partial Least Squares Optimization with Deep Learning." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625174.

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Arablouei, Reza, Stefan Werner, and Kutluyil Dogancay. "Partial-diffusion recursive least-squares estimation over adaptive networks." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714014.

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Oreshkin, Boris, and Mark Coates. "Bootstrapping Particle Filters using Kernel Recursive Least Squares." In 2007 IEEE Aerospace Conference. IEEE, 2007. http://dx.doi.org/10.1109/aero.2007.353043.

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de O. Nunes, Leonardo, Ricardo Merched, and Luiz W. P. Biscainho. "Recursive Least-Squares Estimation of the Evolution of Partials in Sinusoidal Analysis." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366664.

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Hou, Ming, and Brahim Chaib-draa. "Fast Recursive Low-rank Tensor Learning for Regression." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/257.

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In this work, we develop a fast sequential low-rank tensor regression framework, namely recursive higher-order partial least squares (RHOPLS). It addresses the great challenges posed by the limited storage space and fast processing time required by dynamic environments when dealing with large-scale high-speed general tensor sequences. Smartly integrating a low-rank modification strategy of Tucker into a PLS-based framework, we efficiently update the regression coefficients by effectively merging the new data into the previous low-rank approximation of the model at a small-scale factor (feature) level instead of the large raw data (observation) level. Unlike batch models, which require accessing the entire data, RHOPLS conducts a blockwise recursive calculation scheme and thus only a small set of factors is needed to be stored. Our approach is orders of magnitude faster than all other methods while maintaining a highly comparable predictability with the cutting-edge batch methods, as verified on challenging real-life tasks.

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Yibin Zheng. "Recursive least squares image reconstruction." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.987775.

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Malik, Mohammad, Mohammad Hakeem, Imran Ghazi, and Ata-ul-basit Hassan. "Recursive Least Squares Spectrum Estimation." In 2006 IEEE International Symposium on Industrial Electronics. IEEE, 2006. http://dx.doi.org/10.1109/isie.2006.295527.

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Geist, Matthieu, and Olivier Pietquin. "Statistically linearized recursive least squares." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5589236.

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Chansarkar, M. M., and U. B. Desai. "A robust recursive least squares algorithm." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319527.

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Reports on the topic "Recursive partial least squares"

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DREWIEN, CELESTE A. A Parallel Prediction-Augmented Classical Least Squares/Partial Least Squares Hybrid Algorithm: CPLS 1.0 Code. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/759455.

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Faber, V. Partial least squares, conjugate gradient and the fisher discriminant. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/431144.

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Lafreniere, Philip. Application of Partial Least Squares Approaches to Pyroprocessing ER Data. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2460467.

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Cioffi, J. M., and T. Kailath. An Efficient, RLS (Recursive-Least-Squares) Data-Driven Echo Canceller for Fast Initialization of Full-Duplex Data Transmission,. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160177.

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Fuhrmann, Bernd. Application of Two Novel Partial Least Squares-Based Regression Methods to the Analysis of Spectral Datasets. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-229.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Bonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7696531.bard.

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To reduce financial risk and N losses to the environment, fertilization methods are needed that improve NUE and increase the quality of wheat. In the literature, ample attention is given to grid-based and zone-based soil testing to determine the soil N available early in the growing season. Plus, information is available on in-season N topdressing applications as a means of improving GPC. However, the vast majority of research has focused on wheat that is grown under N limiting conditions in sub-humid regions and irrigated fields. Less attention has been given to wheat in dryland that is water limited. The objectives of this study were to: (1) determine accuracy in determining GPC of HRSW in Israel and SWWW in Oregon using on-combine optical sensors under field conditions; (2) develop a quantitative relationship between image spectral reflectance and effective crop physiological parameters; (3) develop an operational precision N management procedure that combines variable-rate N recommendations at planting as derived from maps of grain yield, GPC, and test weight; and at mid-season as derived from quantitative relationships, remote sensing, and the DSS; and (4) address the economic and technology-transfer aspects of producers’ needs. Results from the research suggest that optical sensing and the DSS can be used for estimating the N status of dryland wheat and deciding whether additional N is needed to improve GPC. Significant findings include: 1. In-line NIR reflectance spectroscopy can be used to rapidly and accurately (SEP <5.0 mg g⁻¹) measure GPC of a grain stream conveyed by an auger. 2. On-combine NIR spectroscopy can be used to accurately estimate (R² < 0.88) grain test weight across fields. 3. Precision N management based on N removal increases GPC, grain yield, and profitability in rainfed wheat. 4. Hyperspectral SI and partial least squares (PLS) models have excellent potential for estimation of biomass, and water and N contents of wheat. 5. A novel heading index can be used to monitor spike emergence of wheat with classification accuracy between 53 and 83%. 6. Index MCARI/MTVI2 promises to improve remote sensing of wheat N status where water- not soil N fertility, is the main driver of plant growth. Important features include: (a) computable from commercial aerospace imagery that include the red edge waveband, (b) sensitive to Chl and resistant to variation in crop biomass, and (c) accommodates variation in soil reflectance. Findings #1 and #2 above enable growers to further implement an efficient, low cost PNM approach using commercially available on-combine optical sensors. Finding #3 suggests that profit opportunities may exist from PNM based on information from on-combine sensing and aerospace remote sensing. Finding #4, with its emphasis on data retrieval and accuracy, enhances the potential usefulness of a DSS as a tool for field crop management. Finding #5 enables land managers to use a DSS to ascertain at mid-season whether a wheat crop should be harvested for grain or forage. Finding #6a expands potential commercial opportunities of MS imagery and thus has special importance to a majority of aerospace imaging firms specializing in the acquisition and utilization of these data. Finding #6b on index MCARI/MVTI2 has great potential to expand use of ground-based sensing and in-season N management to millions of hectares of land in semiarid environments where water- not N, is the main determinant of grain yield. Finding #6c demonstrates that MCARI/MTVI2 may alleviate the requirement of multiple N-rich reference strips to account for soil differences within farm fields. This simplicity will be less demanding of grower resources, promising substantially greater acceptance of sensing technologies for in-season N management.

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