Component Analysis and Data Fusion

2015 ◽  
pp. 265-308
Keyword(s):  
Data Fusion ◽  
Component Analysis

scholarly journals Common and distinct variation in data fusion of designed experimental data

Metabolomics ◽  
2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Masoumeh Alinaghi ◽  
Hanne Christine Bertram ◽  
Anders Brunse ◽  
Age K. Smilde ◽  
Johan A. Westerhuis
Keyword(s):  
Experimental Design ◽  
Data Fusion ◽  
Component Analysis ◽  
Biological Data ◽  
Integrative Analysis ◽  
Data Sets ◽  
Metabolomics Data ◽  
Simultaneous Component Analysis ◽  
Multiple Data ◽  
Multiple Data Sets

Abstract Introduction Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept. Objectives In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets. Methods Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design. Results Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA. Conclusions This method provides an improved understanding of the common and distinct variation in response to different experimental factors.

Linked independent component analysis for multimodal data fusion

NeuroImage ◽  
2011 ◽  
Vol 54 (3) ◽  
pp. 2198-2217 ◽  
Author(s):  
Adrian R. Groves ◽  
Christian F. Beckmann ◽  
Steve M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Independent Component Analysis ◽  
Data Fusion ◽  
Component Analysis ◽  
Independent Component ◽  
Multimodal Data ◽  
Multimodal Data Fusion

scholarly journals Ancient human bones studied and compared by near infrared spectroscopy, thermogravimetry and chemometrics

2018 ◽  
Vol 27 (1) ◽  
pp. 6-14 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Mauro Tomassetti ◽  
Remo Bucci ◽  
Simona Izzo ◽  
Francesca Candilio ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Data Fusion ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Human Bones ◽  
Fusion Approach ◽  
Ancient Human Bones

Near infrared spectroscopy and thermogravimetry have been coupled with chemometric exploratory methods in order to investigate ancient (pre-Roman/Roman) human bones from two different necropolises in Central-South Italy (Cavo degli Zucchi and Elea Velia). These findings have been investigated by principal component analysis and they have also been compared with ancient human bones from two Sudanese necropolises (Saggai and Geili). Samples coming from African and European necropolises, mainly differ in two aspects: the burial procedures and their historical period. The ritual applied in the European region involved cremation, while the one applied in the African necropolises did not. Bones from Italian sites (Cavo degli Zucchi and Elea Velia) are Pre-Roman/Roman while the others (from middle Nile) come from the Prehistoric, Meroitic, and Christian Sudanese age. Near infrared spectroscopy and thermogravimetric measures have been analysed either individually or by a mid-level data-fusion approach. Principal component analysis of the near infrared spectroscopy data allowed differentiation between burnt and unburnt samples, while from the scores plots extracted from the principal component analysis model based on the entire derived thermograms, it was possible to recognize the different clusters related to the various dating of samples. The data-fusion analysis led to considerations similar to those obtained from the model based on thermogravimetry data. Finally, instead of inspecting the entire thermogravimetry curves, principal component analysis was carried out on carbonates, total collagen and water losses only. In this case, the data-fusion approach has led to extremely interesting results; in fact, this model clearly shows that samples group in separate clusters in agreement with their age and the different burial rituals.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

2018 ◽  
Vol 50 (1) ◽  
pp. 20-33 ◽  
Author(s):  
Emine Elif Tulay ◽  
Barış Metin ◽  
Nevzat Tarhan ◽  
Mehmet Kemal Arıkan
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Psychiatric Diseases ◽  
Multimodal Neuroimaging ◽  
Neuropsychiatric Diseases ◽  
Combining Data ◽  
Neuroimaging Techniques

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers—especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification—especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

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