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Performance of Classification Analysis: A Comparative Study between PLS-DA and Integrating PCA+LDA

Nurazlina Abdul Rashid, Wan Siti Esah Che Hussain, Abd Razak Ahmad, Fatihah Norazami Abdullah
2019 Mathematics and Statistics  
The performance analysis of the PLS-DA was conducted and compared with PCA+LDA model using different number of variables (p) and different sample sizes (n).  ...  PLS-DA can be considered to have a good and reliable technique to be used when dealing with large datasets for classification task.  ...  Acknowledgement The research would like to thank Research and Industrial Linkage Division UiTM Kedah for financial support to publish this paper.  ... 
doi:10.13189/ms.2019.070704 fatcat:4k62izviz5e2lan56z54qsds7u

So you think you can PLS-DA? [article]

Daniel Ruiz Perez, Giri Narasimhan
2017 bioRxiv   pre-print
In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented  ...  Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models for the synthetic data sets used.  ...  ACKNOWLEDGMENTS This work was partially supported by grants from the Department of Defense Contract W911NF-16-1-0494, NIH grant 1R15AI128714-01, and NIJ grant 2017-NE-BX-0001.  ... 
doi:10.1101/207225 fatcat:llkeokjovbakfa7jbe6hdvt24a

So you think you can PLS-DA?

Daniel Ruiz-Perez, Haibin Guan, Purnima Madhivanan, Kalai Mathee, Giri Narasimhan
2020 BMC Bioinformatics  
PLS-DA in comparison with PCA for different underlying data models.  ...  In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented  ...  Both chemometrics and omics data sets are characterized by large volume, large number of features, noise and missing data [2, 7] . These data sets also often have lot fewer samples than features.  ... 
doi:10.1186/s12859-019-3310-7 pmid:33297937 fatcat:mikgapcg3rdihd76ijyeayab7q

Random projection experiments with chemometric data

Kurt Varmuza, Peter Filzmoser, Bettina Liebmann
2010 Journal of Chemometrics  
For special applications in chemometrics with very large data sets and/or severe restrictions for hardware and software resources, RP is a promising method. capabilities (if packed).  ...  Classification of activity Random projection (RP) has often been used for dimensionality reduction in classification problems with large data sets.  ...  Acknowledgements We thank Ulrich Omasits (ETH Zürich) for work with simulated data, Anton Friedl (Vienna University of Technology) for continuous support in this project, and Katja Hansen (Technical University  ... 
doi:10.1002/cem.1295 fatcat:ell2e6ixdjbo3i5ihdm27vspyq

Multivariate statistics process control for dimensionality reduction in structural assessment

L.E. Mujica, J. Vehí, M. Ruiz, M. Verleysen, W. Staszewski, K. Worden
2008 Mechanical systems and signal processing  
This paper presents advantages of using techniques like principal component analysis (PCA), partial least square (PLS) and some extensions called multiway PCA (MPCA) and multiway PLS (MPLS) for reducing  ...  The methodology used for detecting and locating the impact uses the philosophy of case-based reasoning, where single PCA and PLS are used also for organizing previous knowledge in memory.  ...  The authors would like to thank the support to Gurkan Sin who has contributed with new ideas. We are grateful also to Ms Jenny LeClerc who has collected the data within her Master Thesis.  ... 
doi:10.1016/j.ymssp.2007.05.001 fatcat:5vbbmtpevfdvvpatsc56qvihx4

Differential Principal Components Reveal Patterns of Differentiation in Case/Control Studies [article]

Benjamin Lengerich, Eric P Xing
2019 bioRxiv   pre-print
We apply drPCA to several cancer gene expression datasets and find that it more accurately summarizes oncogenic processes than do standard methods such as PCA and PLS-DA.  ...  Methods of supervised discriminant analysis such as partial least squares (PLS-DA) effectively separate conditions, but are hamstrung by inflexibility and overfit to sample labels.  ...  Funding This work is supported by the National Institutes of Health grants R01-GM093156 and P30-DA035778. Bibliography  ... 
doi:10.1101/545798 fatcat:ikzsp3mutzdh7pnmqd736ye4fu

Partial least squares for discrimination in fMRI data

Anders H. Andersen, William S. Rayens, Yushu Liu, Charles D. Smith
2012 Magnetic Resonance Imaging  
Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups  ...  PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination.  ...  Acknowledgments This work was supported by a grant from the National Institute of Neurological Disorders and Stroke (R01-NS036660).  ... 
doi:10.1016/j.mri.2011.11.001 pmid:22227352 pmcid:PMC3288364 fatcat:xlaqhk23grb7fnt2uuxis77qp4

Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)

Anne-Laure Boulesteix
2006 Statistical Applications in Genetics and Molecular Biology  
This note is a comment on the article "Dimension Reduction for Classification with Gene Expression Microarray Data" that appeared in Statistical Applications in Genetics and Molecular Biology (Dai et al  ...  studies on PCA and PLS.  ...  At last, I would like to discuss briefly some other dimension reduction methods that are related to PCA, PLS and SIR and have been used in the context of tumor classification with microarray data analysis  ... 
doi:10.2202/1544-6115.1226 pmid:17049027 fatcat:vbau7zqecfb2hfyav4k4u6sqyq

Multivariate Analysis of ToF-SIMS Data from Multicomponent Systems: The Why, When, and How

Daniel J. Graham, David G. Castner
2012 Biointerphases  
Herein we discuss the application of PCA and other MVA methods to multicomponent ToF-SIMS data and provide guidelines on their application and use.  ...  MVA presents a powerful set of tools to aid the user in processing data from complex, multicomponent surfaces such as biological materials and biosensors.  ...  These methods are a way for the analyst to summarize and understand the large data sets generated by ToF-SIMS.  ... 
doi:10.1007/s13758-012-0049-3 pmid:22893234 pmcid:PMC3801192 fatcat:xqismddyzzcrhbk2vtjnyqh3im

3-Way and 3-block PLS regressions in consumer preference analysis

Valérie Lengard, Martin Kermit
2006 Food Quality and Preference  
The data used to illustrate these two powerful methods is a set of 17 tomato varieties with information acquired from chemical measurements, sensory panel evaluations and consumer likings, attitudes and  ...  PLS regression modelling using 3-way or 3-block data is sometimes misleadingly regarded as a single method.  ...  Data reduction and exploratory analysis with PCA In some cases, when the data set contains a large number of dependent variables, it may prove useful to reduce the data set into smaller segments to provide  ... 
doi:10.1016/j.foodqual.2005.05.005 fatcat:aavbvyxjmzgkpgq3utxpjk75gy

Principal Model Analysis Based on Partial Least Squares [article]

Qiwei Xie and Liang Tang and Weifu Li and Vijay John and Yong Hu
2019 arXiv   pre-print
In the proposed PMA algorithm, the PCA and the PLS are combined.  ...  In the method, multiple PLS models are trained on sub-training sets, derived from the original training set based on the random sampling with replacement method.  ...  The general PLS method usually shows bad or unstable results on the data with a very large number of collinear x-variables or the data with very limited training samples.  ... 
arXiv:1902.02422v1 fatcat:554yjf5jmvhc5j6et4vp63m7my

Supervised pattern recognition in food analysis

Luis A. Berrueta, Rosa M. Alonso-Salces, Károly Héberger
2007 Journal of Chromatography A  
and errors that might arise.  ...  The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions  ...  UNEQ requires homogeneous populations and is very sensitive to unbalanced data sets, and that the ratio of objects/variables is sufficiently high, at least 3.  ... 
doi:10.1016/j.chroma.2007.05.024 pmid:17540392 fatcat:l5za2knhujhwzk3v4wrqdi2zzy

Regression models tolerant to massively missing data: a case study in solar radiation nowcasting

I. Žliobaitė, J. Hollmén, H. Junninen
2014 Atmospheric Measurement Techniques Discussions  
Due to the need to provide instantaneous outputs with minimum energy consumption for computing in the data streaming setting, we dismiss computationally demanding data imputation methods, and resort to  ...  We experimentally analyze accuracies and robustness to missing data of seven linear regression models and recommend using regularized PCA regression.  ...  This work has been supported by the Academy of Finland grant 118653 (ALGODAN) and grant 258568 (MultiTree). Edited by: M. Weber  ... 
doi:10.5194/amtd-7-7137-2014 fatcat:rujn4npka5cdxee7s3lmnwjx3m

Regression models tolerant to massively missing data: a case study in solar-radiation nowcasting

I. Žliobaitė, J. Hollmén, H. Junninen
2014 Atmospheric Measurement Techniques  
Due to the need to provide instantaneous outputs with minimum energy consumption for computing in the data streaming setting, we dismiss computationally demanding data imputation methods and resort to  ...  We are after one model that performs well at all times, with and without data gaps.  ...  This work has been supported by the Academy of Finland grant 118653 (ALGODAN) and grant 258568 (MultiTree). Edited by: M. Weber  ... 
doi:10.5194/amt-7-4387-2014 fatcat:qruch3dfdfckvl2yw3j3f2dq7u

Dimension Reduction for Classification with Gene Expression Microarray Data

Jian J Dai, Linh Lieu, David Rocke
2006 Statistical Applications in Genetics and Molecular Biology  
This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates  ...  Predictive accuracy and computational efficiency of the methods are examined. Two gene expression data sets for tumor classification are used in the study.  ...  PLS is computationally very efficient with cost only at O(np), i.e. the number of calculations required by PLS is a linear function of n and p.  ... 
doi:10.2202/1544-6115.1147 pmid:16646870 fatcat:lx3uzic75bdtdcgrvgqy6xljja
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