Statistical Learning Methods Including Dimensionality Reduction

2007 ◽  
Vol 52 (1) ◽  
pp. 370-373 ◽  
Author(s):  
Hans-Hermann Bock ◽  
Maurizio Vichi
Keyword(s):  
Dimensionality Reduction ◽  
Statistical Learning ◽  
Learning Methods

Multicopter Fault Detection and Identification via Data-Driven Statistical Learning Methods

AIAA Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Airin Dutta ◽  
Michael E. McKay ◽  
Fotis Kopsaftopoulos ◽  
Farhan Gandhi
Keyword(s):  
Fault Detection ◽  
Statistical Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Detection And Identification ◽  
Fault Detection And Identification

scholarly journals Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Jinyu Zang ◽  
Yuanyuan Huang ◽  
Lingyin Kong ◽  
Bingye Lei ◽  
Pengfei Ke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Cross Validation ◽  
Integrated Model ◽  
Machine Learning Techniques ◽  
Brain Atlas ◽  
First Episode ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Brain Atlases

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

scholarly journals Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

2018 ◽  
Vol 12 ◽  
pp. 117793221875929 ◽  
Author(s):  
Irene Sui Lan Zeng ◽  
Thomas Lumley
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Information Science ◽  
Molecular System ◽  
Principal Component ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Integrated Omics ◽  
Types Of Information ◽  
Integrated Information

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Effective Statistical Learning Methods for Actuaries I

2019 ◽  
Author(s):  
Michel Denuit ◽  
Donatien Hainaut ◽  
Julien Trufin
Keyword(s):  
Statistical Learning ◽  
Learning Methods
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