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A theoretical investigation of several model selection criteria for dimensionality reduction
2012
Pattern Recognition Letters
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Based on the problem of determining the hidden dimensionality (or the number of latent factors) of Factor Analysis (FA) model, this paper provides a theoretic comparison on several classical model selection ...
This order indicates an order of model selection performances to great extent, because underestimations usually take the major proportion of wrong selections when the sample size and the population signal-to-noise ...
Acknowledgements The work described in this paper was fully supported by a grant from the Research Grant Council of the Hong Kong SAR (Project No: CUHK418012E). ...
doi:10.1016/j.patrec.2012.01.010
fatcat:i3z2abr43rffxl5tf4yp3oze4a
Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization
2000
IEEE Transactions on Neural Networks
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To reveal all of the interesting aspects of multimodal data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized ...
component neural networks under the information theoretic criteria. ...
Li of the University of Maryland at College Park and T. Adali of the University of Maryland at Baltimore County for their valuable scientific input to this work. ...
doi:10.1109/72.846734
pmid:18249790
fatcat:ucdmszpqg5g7titl3wizzfgrru
UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA
2023
Global Multidisciplinary Journal
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In the realm of data analysis, the exploration of independent component analysis (ICA) for tensor-valued data represents a burgeoning area of research. ...
This paper delves into the application of ICA techniques specifically tailored for tensor-valued data, exploring theoretical foundations, algorithmic implementations, and practical considerations. ...
This involves addressing preprocessing steps, dimensionality reduction techniques, and model selection criteria relevant to the analysis of multidimensional datasets. ...
doi:10.55640/gmj-abc114
fatcat:wo3f27745rhwjix43rtb6uyn54
Theoretical Analysis and Comparison of Several Criteria on Linear Model Dimension Reduction
[chapter]
2009
Lecture Notes in Computer Science
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Detecting the dimension of the latent subspace of a linear model, such as Factor Analysis, is a well-known model selection problem. ...
Aiming at a theoretical analysis and comparison of different criteria, we formulate a tool to obtain an order of their approximate underestimation-tendencies, i.e., AIC, BIC/MDL, CAIC, BYY-FA(a), from ...
The work described in this paper was fully supported by a grant from the Research Grant Council of the Hong Kong SAR (Project No: CUHK4177/07E). ...
doi:10.1007/978-3-642-00599-2_20
fatcat:wee3qpihojhojieksw24cma4ce
Determination of instability of a DP 980 steel sheet under different stress states based on experiment and theoretical models
2016
MATEC Web of Conferences
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The formability features of the studied steel in whole strain ratio range and differences among the investigated theoretical models were finally discussed. ...
The results indicate that the M-K instability model shows better prediction of the studied steel compared to the other models investigated in this research. ...
Acknowledgement The support of CSC (Chinese scholarship council) is greatly appreciated. ...
doi:10.1051/matecconf/20168003007
fatcat:2ms6yxh4pjfolotblc4rycmexe
Predicting the risk of psychosis onset: advances and prospects
2012
Early Intervention in Psychiatry
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Aim-To conduct a systematic review of the methods and performance characteristics of models developed for predicting the onset of psychosis. ...
reduction methods and predictive model algorithms like the support vector machine (SVM). ...
Library of Medicine grant HHSN276201000030C. ...
doi:10.1111/j.1751-7893.2012.00383.x
pmid:22776068
pmcid:PMC3470783
fatcat:e2izutbi5zhehpliut2nzgonvy
Virtual sensing for gearbox condition monitoring based on kernel factor analysis
2017
Petroleum Science
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feature selection techniques in terms of virtual sensing model accuracy. ...
However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process. ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s12182-017-0163-4
fatcat:tkmufv3q3ffgbg62so6sx7cgya
Virtual sensing for gearbox condition monitoring based on extreme learning machine
2017
Journal of Vibroengineering
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Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving ...
To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation ...
The authors would like to thank the anonymous reviewers for their constructive comments, which have helped improve the paper. ...
doi:10.21595/jve.2016.17379
fatcat:vjvx3ezdgjbajmuyqphw3bqiwi
Tandem clustering with invariant coordinate selection
[article]
2024
arXiv
pre-print
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Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. ...
The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. ...
This work was partly supported by a grant of the Dutch Research Council (NWO, research program Vidi, project number VI. ...
arXiv:2212.06108v4
fatcat:tchfpuz43jfibptzljowlo4wcq
Order Selection of the Linear Mixing Model for Complex-Valued FMRI Data
2010
Journal of Signal Processing Systems
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In this work, we develop a complex-valued order selection method to estimate the dimension of signal subspace using information-theoretic criteria. ...
To correct the effect of sample dependence to information-theoretic criteria, we develop a general entropy rate measure for complex Gaussian random process to calibrate the independent and identically ...
and identify a subset of effectively i.i.d. samples to correctly calculate information-theoretic criteria for selecting model order. ...
doi:10.1007/s11265-010-0509-2
pmid:23750289
pmcid:PMC3673748
fatcat:o6oukoctq5aytbj337usbe7y5u
A Review of Flow Forming Processes and Mechanisms
2015
Key Engineering Materials
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Theoretical and experimental approaches are collected and compared evaluating their prediction models. Several knowledge gaps can be identified. ...
The review surveys academic paper of last fifty years, in order to evaluate the current state of art for academic and practitioner. ...
Analytical methodologies aim to develop a theoretical model in order to forecast the flow of the metal during the process. ...
doi:10.4028/www.scientific.net/kem.651-653.750
fatcat:qiq5opxx6vhbfnu4shxyxo5fiy
Review of Dimensionality Reduction Techniques in Data Mining from Big Data
2019
International Journal for Research in Applied Science and Engineering Technology
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This research paper represents a comprehensive review of diverse methods that are applied for the process of big data reduction and conjointly presents a comprehensive discussion on big data dimension ...
reduction processes, redundancy elimination, automatic learning process, data extraction, size or volume reduction, and big data compression. ...
Several techniques and models for PCA for data reduction have been proposed [5] . Maximum likelihood approach is proposed by Zhai et al (2014) [8] . ...
doi:10.22214/ijraset.2019.5359
fatcat:4goblbok35hm7dbqbhbphcljga
A Review on Dimensionality Reduction Techniques
2017
International Journal of Computer Applications
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Feature selection and feature extraction techniques as a preprocessing step are used for reducing data dimensionality. ...
This paper analyses some existing popular feature selection and feature extraction techniques and addresses benefits and challenges of these algorithms which would be beneficial for beginners.. ...
It describes several tools and techniques for reducing dimensionality of data. ...
doi:10.5120/ijca2017915260
fatcat:2sfd5rzh6bafnpsswnedtugdh4
A Comparison of Variables Selection Methods and their Sequential Application: A Case Study of the Bankruptcy of Polish Companies
2020
Folia Oeconomica Stetinensia
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paper compares different variable selection methods and demonstrates the effectiveness of their sequential application for dimensionality reduction. ...
: This work aims to compare different variable selection approaches and introduce a new methodology of sequential variable selection that can be applied when the low-dimensional model is preferred.Research ...
The second hypothesis is that the sequential application of different variable selection methods can allow getting a higher reduction in dimensionality than a single model approach. ...
doi:10.2478/foli-2020-0031
fatcat:zskobr5fl5edjoc2ox5dbibzqm
Quantifying relationships between selected work-related risk factors and back pain: A systematic review of objective biomechanical measures and cost-related health outcomes
2009
International Journal of Industrial Ergonomics
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The objective of this investigation was to use published literature to demonstrate that specific changes in workplace biomechanical exposure levels can predict reductions in back injuries. ...
A systematic literature review was conducted to identify epidemiologic studies which could be used to quantify relationships between several well-recognized biomechanical measures of back stress and economically ...
National Institutes of Health, grant number 1R43AR52565-1A2. ...
doi:10.1016/j.ergon.2008.06.003
pmid:20047008
pmcid:PMC2662685
fatcat:pixwuikhizhs5jhxgmetjd6iim
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