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Effect of Dimension Reduction on Prediction Performance of Multivariate Nonlinear Time Series

Jun-Yong Jeong, Jun-Seong Kim, Chi-Hyuck Jun
2015 Industrial Engineering & Management Systems  
We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction.  ...  The dynamic system approach in time series has been used in many real problems.  ...  Thus, the goal of this paper is to analyze the effect of dimension reduction on prediction of multivariate nonlinear time series; we select LSSVR to approximate the predictive function because of structural  ... 
doi:10.7232/iems.2015.14.3.312 fatcat:6dbpwtm2fjawnh7nncrvtjz2zu

Eigenanatomy: Sparse dimensionality reduction for multi-modal medical image analysis

Benjamin M. Kandel, Danny J.J. Wang, James C. Gee, Brian B. Avants
2015 Methods  
These dimensionality reduction techniques, however, consist of contributions from every region in the brain and are therefore difficult to interpret.  ...  The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal component analysis (PCA) or independent component analysis (ICA).  ...  The authors would like to acknowledge the support of the following grants: R01-MH080892, R01-NS081077, R01-EB014922 (DW), T32-EB009384, P30-NS045839, R01-NS065347, R01-EB006266,  ... 
doi:10.1016/j.ymeth.2014.10.016 pmid:25448483 pmcid:PMC4329066 fatcat:lyqrirgdifa7nbibvgzk42d5x4

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

A.-L. Boulesteix, K. Strimmer
2006 Briefings in Bioinformatics  
Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of high-dimensional genomic data.  ...  Focusing on microarray expression data we provide a systematic comparison of the PLS approaches currently employed, and discuss problems as different as tumor classification, identification of relevant  ...  This approach is reported to lead to high prediction accuracy, although it seems rather unappealing to predict categorical responses using a classical linear model.  ... 
doi:10.1093/bib/bbl016 pmid:16772269 fatcat:nnjyr7s7xvaazfndg6wziieftq

Enhanced-PCA based Dimensionality Reduction and Feature Selection for Real-Time Network Threat Detection

P. More, P. Mishra
2020 Engineering, Technology & Applied Science Research  
The proposed approach makes a strong case as a dimensionality reduction and feature selection technique for minimizing false detection rates when operating on real-time data.  ...  The proposed method is found to be better than the standard PCA and FAST methods for data reduction.  ...  CONCLUSION In this paper, a new approach was presented for the dimensionality reduction and the subsequent prediction of any intrusion threats to a network system.  ... 
doi:10.48084/etasr.3801 fatcat:gfea6qsal5f25dd23aicqle37i

Bayesian Supervised Dimensionality Reduction

Mehmet Gonen
2013 IEEE Transactions on Cybernetics  
However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance.  ...  We present both Gibbs sampling and variational approximation approaches to learn the proposed probabilistic model for multiclass classification.  ...  We propose to combine linear dimensionality reduction and linear supervised learning in a joint probabilistic model in order to obtain a more predictive subspace.  ... 
doi:10.1109/tcyb.2013.2245321 pmid:23757527 fatcat:f3alxycqxfgptlq2wyrbqnvkm4

Large-scale Granger causality analysis on resting-state functional MRI

Adora M. D'Souza, Anas Zainul Abidin, Lutz Leistritz, Axel Wismüller, Barjor Gimi, Andrzej Krol
2016 Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging  
By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at  ...  an individual voxel level, unlike other multivariate approaches.  ...  The limitation with using a multivariate approach to Granger Causality, without incorporating dimensionality reduction, on large systems like the brain, is that the prediction model cannot estimate model  ... 
doi:10.1117/12.2217264 pmid:29170585 pmcid:PMC5697152 dblp:conf/mibam/DSouzaALW16 fatcat:oe5u6twzhverbckgxkkrr6yyw4

Multivariate strategies in functional magnetic resonance imaging

L HANSEN
2007 Brain and Language  
In a case study we analyze linear and non-linear dimensional reduction tools in the context of a 'mind reading' predictive multivariate fMRI model.  ...  We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction.  ...  Thus, we need to consider the representation carefully for example by an initial dimensional reduction step prior to modeling.  ... 
doi:10.1016/j.bandl.2006.12.004 pmid:17223190 fatcat:jbprl2o2p5azdoem55k6dk3ijm

Guest Editor's Introduction to the Special Issue on "Modern Dimension Reduction Methods for Big Data Problems in Ecology"

Christopher K. Wikle, Scott H. Holan, Mevin B. Hooten
2013 Journal of Agricultural Biological and Environmental Statistics  
They provide a computationally feasible approach for drawing inference in such environments in a manner that accommodates dimension reduction in both the parameter and data space.  ...  In particular, they are concerned with the issue of inference in a high-dimensional multivariate spatial setting with spatial nonstationarity.  ...  In addition to the high-dimensional multivariate outcome in space, they are also concerned with accommodating spatially extensive covariates, such as those associated with topography and census information  ... 
doi:10.1007/s13253-013-0151-0 fatcat:xr7kbm5u7zcaddl24dd5rve6ja

Convenient optimization strategy implemented in multivariable predictive control

Marek Kubalcik, Vladimir Bobal, Tomas Barot, N. Mastorakis, V. Mladenov, A. Bulucea
2018 MATEC Web of Conferences  
A significantly important part of model predictive control (MPC) with constraints is a solution of an optimization task.  ...  In practical realization of a multivariable MPC, the optimization is characterized by higher computational complexity.  ...  As evaluation of all defined constraints is significantly timedemanding in multivariable MPC, the proposed modification of the Wang's approach can be advantageous due to significant reduction of numerical  ... 
doi:10.1051/matecconf/201821002022 fatcat:kgjlm6zotbcedfwuajh3ju6rwe

Application of discriminate analysis to prediction of company future economic development

Radmila Sousedíková, Jaroslav Dvořáček, Igor Savič
2012 GeoScience Engineering  
An outlier reduction of original data files brings data distribution closer to multivariate normality, and substantially improves discriminate function classification abilities  ...  The paper takes into account applications of discriminate analysis as regards prediction of future economic development of companies.  ...  Altman (1968) built on the existing results and could overcome limitations of one-dimensional approaches by combining several financial ratios for his developments of prediction models.  ... 
doi:10.2478/v10205-011-0021-3 fatcat:xrzhf5tsvbfgvar2yjb5e5eufi

PCA-SIR: A new nonlinear supervised dimension reduction method with application to pain prediction from EEG

Yiheng Tu, Yeung Sam Hung, Li Hu, Zhiguo Zhang
2015 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)  
Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data.  ...  Dimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI.  ...  The level of subjective pain perception was then predicted using multivariate regression PCA-SIR: A New Nonlinear Supervised Dimension Reduction Method with Application to Pain Prediction from EEG  ... 
doi:10.1109/ner.2015.7146796 dblp:conf/ner/TuHHZ15 fatcat:yxv3orfyc5cspcrp6t5qdswa5y

Time series prediction by chaotic modeling of nonlinear dynamical systems

Arslan Basharat, Mubarak Shah
2009 2009 IEEE 12th International Conference on Computer Vision  
Our main contributions are: multivariate phase space reconstruction for human actions and dynamic textures, a deterministic approach to model dynamics in contrast to the popular noise-driven approaches  ...  We extract this information from the strange attractor and utilize it to predict future observations.  ...  In the second case, multivariate prediction approach is used to evolve the predictions in an even higher dimensional phase space (order of P -dimensional).  ... 
doi:10.1109/iccv.2009.5459429 dblp:conf/iccv/BasharatS09 fatcat:x5lo2qulrfeg3f2zsl7dihauiy

Power analysis attack: an approach based on machine learning

Liran Lerman, Gianluca Bontempi, Olivier Markowitch
2014 International Journal of Applied Cryptography  
This paper explores the use of machine learning techniques to relax such assumption and to deal with high dimensional feature vectors.  ...  However, these techniques rely on parametric assumptions and are often limited to small dimensionality settings, which limit their range of application.  ...  Acknowledgements The authors would like to thank Filip Demaertelaere and Cédric Meuter for their support.  ... 
doi:10.1504/ijact.2014.062722 fatcat:5ecivt4vgrdb3gubdqpsu53pmm

The Multivariate Generalised von Mises Distribution: Inference and Applications

Alexandre Navarro, Jes Frellsen, Richard Turner
2017 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Previously proposed multivariate circular distributions are shown to be special cases of this construction.  ...  This distribution can be constructed by restricting and renormalising a general multivariate Gaussian distribution to the unit hyper-torus.  ...  Dimensionality reduction To demonstrate the dimensionality reduction application, we analysed two datasets: one motion capture dataset comprising marker positions placed on a subject's arm and captured  ... 
doi:10.1609/aaai.v31i1.10943 fatcat:yeutp4jopzaizeilesqpvfwqsy

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

Carlos Carvalho, Danielo G. Gomes, Nazim Agoulmine, José Neuman de Souza
2011 Sensors  
To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction.  ...  This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN).  ...  Acknowledgments The authors would like to thank the Brazilian funding agencies FAPEPI (Ph.D. Scholarship) and CNPq for their financial support.  ... 
doi:10.3390/s111110010 pmid:22346626 pmcid:PMC3274268 fatcat:f34wx2cacbcftfprunlrgf2jq4
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