26 papers · ranked by Valyu relevance
Alexander von Eye, Wolfgang Wiedermann
Unless very large samples are available, the number of variables and variable categories that can be simultaneously used in categorical data analysis is small when models are estimated. In this article, an approach is proposed that can help remedy this problem. Specifically, it is proposed to perform, in a first step…
Muehlmann, Christoph, Fačevicová, Kamila + 6 more
Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the application of standard multivariate analysis tools on the raw…
Carlos Óscar S. Sorzano, Javier Vargas, Alberto Pascual-Montano
—Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging…
Zenon Gniazdowski
The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors…
Viktorie Nesrstová, Ines Wilms, Karel Hron, Peter Filzmoser
Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible pairs to consider quickly becomes too large even for medium-sized…
Ju‐Chi Yu, Julie Le Borgne, Anjali Krishnan, Arnaud Gloaguen + 4 more
Generalized Singular Value Decomposition Authors: ['Ju‐Chi Yu' 'Julie Le Borgne' 'Anjali Krishnan' 'Arnaud Gloaguen' 'Cheng‐Ta Yang' 'Laura A. Rabin' 'Hervé Abdi' 'Vincent Guillemot'] Correspondence analysis, multiple correspondence analysis and their discriminant counterparts (i.e., discriminant simple correspondence…
Bingkai Wang, Xi Luo, Yi Zhao, Brain Caffo
We consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual specific. This paper proposes consistent estimators of shared eigenvectors…
Christoph Sperber
For years, dissociation studies on neurological single cases with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value and even misleading. Still, principal component analysis…
Peng Peng, Ivens Portugal, Paulo Alencar, Donald Cowan + 1 more
analysis A face recognition software framework based on principal component analysis Authors: ['Peng Peng' 'Ivens Portugal' 'Paulo Alencar' 'Donald Cowan' 'Robertas Damaševičius'] Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military…
Ronald Fischer, Johannes A. Karl
Exploratory factor analysis as a group of statistical techniques is in many ways similar to CFA, but it does not presuppose a theoretical structure. EFA is often used as a first estimation of the factor structure, which can be confirmed in subsequent studies with CFA. Alternatively, researchers may use EFA to…
Authors not listed
Vaccination is a highly efficient strategy in controlling infections. Aluminum-containing adjuvants have long been used to enhance immunogenicity, but quantification of adsorbed antigens remains analytically challenging. This study uses Raman spectroscopy, a powerful, non-destructive technique, augmented by machine…
Viktorie Nesrstová, Ines Wilms, Karel Hron, Peter Filzmoser
Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible pairs to consider quickly becomes (too) large even for medium-sized…
Katrijn Van Deun, Tom F Wilderjans, Robert A van den Berg, Anestis Antoniadis + 1 more
'Anestis Antoniadis' 'Iven Van Mechelen'] 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is…
Ingrid Måge, Christina Steppeler, Ingunn Berget, Jan Erik Paulsen + 1 more
This paper presents a strategy for statistical analysis and interpretation of longitudinal intervention effects on bacterial communities. Data from such experiments often suffers from small sample size, high degree of irrelevant variation, and missing data points. Our strategy is a combination of multi-way…
Denis Polunin, Irina Shtaiger, Vadim Efimov
Biologists more and more have to deal with objects with non-numeric descriptions: texts (e.g. genetic sequences or even whole genomes), graphs, images, etc. There even could be no variables or descriptions at all when variability of objects is defined by similarity matrix. It is also possible to have too many variables…
Huiwen Yu, Lili Guo, Mourad Kharbach, Wenjie Han + 2 more
'Ernestina Casiraghi' 'Silvia Grassi'] Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the…
Xi Yu, Florian Schuberth, Jörg Henseler
Composites, which refer to weighted linear combinations of variables, are receiving increasing attention in the field of ecology. In practice, however, researchers relying on the common approaches to study composites encounter limitations in flexibly specifying composites with structural equation modeling (SEM). To…
Keisuke Ozawa
Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of…
Suk-Heung Song, Herb Ryan, Jens Hoefflin, Taeyoon Kyung + 3 more
Analytical technologies for engineered biological systems hold great promise in addressing various challenges in modern pharmaceuticals and biomedical therapies. These endeavors often follow a design-build-test-learn approach, utilizing biological data from genetic circuits, signal pathways, metabolites, and proteins…
Maxwell Venetos, Masha Elkin, Connor Delaney, John Hartwig + 1 more
NMR spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity to manually analyze NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons…
Thomas P. Quinn, Ionas Erb
In the health sciences, many data sets produced by next-generation sequencing (NGS) only contain relative information because of biological and technical factors that limit the total number of nucleotides observed for a given sample. As mutually dependent elements, it is not possible to interpret any component in…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
Ruiping Liu, Ndèye Niang, Gilbert Saporta, Huiwen Wang
Since the introduction of the lasso in regression, various sparse methods have been developed in an unsupervised context like sparse principal component analysis (s-PCA), sparse canonical correlation analysis (s-CCA) and sparse singular value decomposition (s-SVD). These sparse methods combine feature selection and…
Victor P. Andreev, Gang Liu, Jarcy Zee, Lisa Henn + 2 more
Biological, ecological, social, and technological systems are complex structures with multiple interacting parts, often represented by networks. Correlation matrices describing interdependency of the variables in such structures provide key information for comparison and classification of such systems. Classification…
Khoa Huynh, Karen Feilberg, Jonas Sundberg
Carboxylic acids are a small but important compound class within petroleum chemistry, contributing significantly to the behavior of crude oil, e.g., in production, processing, and its environmental impact. A more detailed structural information is fundamental to improve our understanding of their influence on petroleum…
Anthony Onwuli, Keith T. Butler, Aron Walsh
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and…