scholarly journals Discrimination of Salix caprea, Salix gracilistyla, and Their Interspecific Hybrid Using Vegetative Characteristics and Partial Least Squares Discriminant Analysis

HortScience ◽  
2021 ◽  
pp. 1-9
Author(s):  
Han-Na Seo ◽  
Hyo-In Lim ◽  
Yong-Yul Kim ◽  
Seung-Beom Chae ◽  
Wonwoo Cho
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Interspecific Hybrid ◽  
Morphological Characteristics ◽  
Classification Performance ◽  
Species Discrimination ◽  
Breeding Programs ◽  
Salix Caprea ◽  
Salix Gracilistyla ◽  
Hybrid Variety

Identifying the morphological characteristics that distinguish plant varieties is an important issue for plant breeders and researchers. The objective of the present study was to create a partial least squares discrimination analysis (PLS-DA) model with morphological characteristics for species discrimination and to select the characteristics most important for species discrimination. Data for 27 vegetative characteristics were obtained from Salix caprea and Salix gracilistyla, and their interspecific hybrid (S. caprea × S. gracilistyla), and used for PLS-DA. According to this analysis, seven of the 27 characteristics were identified as those that most influenced species discrimination, and the PLS-DA model with these seven characteristics had a classification accuracy of 86% to 100%. The classification performance of this model was not significantly different from that of the model with all 27 characteristics (full model). Therefore, these results indicated that the three species can be relatively well distinguished by the seven characteristics extracted by PLS-DA. In addition, the selected characteristics can be used to select cross-breeding parents in subsequent breeding programs and to test the distinction, uniformity, and stability (DUS test) of the hybrid variety. From this perspective, PLS-DA is thought to be a useful methodology for classifying new plant varieties and providing information for breeding.

An fMRI study examining spatial navigation through a virtual environment: Partial least squares analysis

2005 ◽  
Author(s):  
Richard Mraz ◽  
Nancy J. Lobaugh ◽  
Genevieve Quintin ◽  
Konstantine K. Kakzanis ◽  
Simon J. Graham
Keyword(s):  
Least Squares ◽  
Virtual Environment ◽  
Partial Least Squares ◽  
Spatial Navigation ◽  
Partial Least Squares Analysis ◽  
Fmri Study

Controlling in der Produktentwicklung

Controlling ◽  
2020 ◽  
Vol 32 (3) ◽  
pp. 45-50
Author(s):  
Marc Janka
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Finite Mixture

Gemeinhin gilt die Annahme, dass das Controlling für viele deutsche Unternehmen auch oder besonders in der Produktentwicklung von großer Bedeutung ist und vor allem unter Umfeldunsicherheit ein wesentlicher Erfolgsfaktor sein kann. Der vorliegende Beitrag zeigt unter Anwendung einer für die Controlling-Forschung neuartigen Methode zur Schätzung von Mischverteilungen mittels partieller Regressionen (englisch finite mixture partial least squares [FIMIX-PLS]), ob diese Annahme für alle Unternehmen gleichermaßen gilt.

Partial Least Squares Strukturgleichungsmodellierung

2017 ◽  
Author(s):  
Joseph F. Hair ◽  
Sven Hauff ◽  
G. Tomas M. Hult ◽  
Nicole F. Richter ◽  
Christian M. Ringle ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

THE EFFECT OF WORKLOAD AND SUPERVISION ON WORK STRES AND PERFORMANCE : A STUDY OF PRODUCTION DEPARTMENT ON LAMONGAN SHIPYARD

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Rezha Isyraqi Qastalano ◽  
Bambang Syairudin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Work Stress ◽  
Production Department ◽  
And Performance

The purpose of the study was to identify the effect of workload and supervision on work stres and performance. Sample determination used production department employees in Lamongan Shipyard, which made a total of 134 respondents. The method used partial least squares (PLS). Results of the analysis showed that workload had positively and significantly affect on work stress; workload had positively and significantly affect on performance; supervision had positively and significantly affect on work stress; supervision had positively and significantly affect on work stress; supervision had positively and not significantly affect on performance and  lastly, work stress positively and had not significantly affect on performance

Sign in / Sign up

Export Citation Format

Share Document