Search · four archives
Search · four archives
26 papers · ranked by Valyu relevance
Jonathon Shlens
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works.…
Felipe L. Gewers, Gustavo Rodrigues Ferreira, Henrique Ferraz de Arruda, Filipi N. Silva + 3 more
'Henrique Ferraz de Arruda' 'Filipi N. Silva' 'César H. Comin' 'Diego R. Amancio' 'Luciano da Fontoura Costa'] Principal component analysis (PCA) is often used for analysing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by…
О. В. Мельников, Loren Hopkins, Katherine B. Ensor
The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration…
Paul F. Smith, Yiwen Zheng
Disorders of sensory systems, as with most disorders of the nervous system, usually involve the interaction of multiple variables to cause some change, and yet often basic sensory neuroscience data are analyzed using univariate statistical analyses only. The exclusive use of univariate statistical procedures, analyzing…
James A. Grant-Jacob, Michalis N. Zervas, Ben Mills
The appearance of plasma generated during femtosecond laser machining depends strongly on the features present on the sample before machining occurs. However, the complexity of femtosecond light-matter interaction means that development of a theoretical understanding of plasma generation is challenging. In this work…
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…
César Roberto de Souza
This documents aims to clarify frequent questions on using the Accord.NET Framework to perform statistical analyses. Here, we reproduce all steps of the famous Lindsay's Tutorial on Principal Component Analysis, in an attempt to give the reader a complete hands-on overview on the framework's basics while also…
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…
Ruman Gerst, Martin Hölzer
The initial characterization and clustering of biological samples is a critical step in the analysis of any transcriptomics study. In many studies, principal component analysis (PCA) is the clustering algorithm of choice to predict the relationship of samples or cells based solely on differential gene expression. In…
Kevin L. Mills, James J. Filliben
Experimenters characterize the behavior of simulation models for data communications networks by measuring multiple responses under selected parameter combinations. The resulting multivariate data may include redundant responses reflecting aspects of a smaller number of underlying behaviors. Reducing the dimension of…
S. Park, E. Ceulemans, K. Van Deun
Principal component analysis (PCA) is an important tool for analyzing large collections of variables. It functions both as a pre-processing tool to summarize many variables into components and as a method to reveal structure in data. Different coefficients play a central role in these two uses. One focuses on the…
Maria Carilli, Kayla Jackson, Lior Pachter
Contrastive learning methods can be powerful tools for genomics, enabling the identification of signals in an experiment via dimension reduction while reducing noise using a control. One such popular approach is contrastive PCA, which, despite being used in a variety of settings, does not scale to large datasets. We…
Maria Tufariello, Sandra Pati, Lorenzo Palombi, Francesco Grieco + 2 more
This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and…
Stephen Pankavich, Rebecca Swanson
Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields…
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…
Mirela Praisler, Stefanut Ciochina
An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA) is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their…
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…
Doaa Youssef, Salah Hassab-Elnaby, Hatem El-Ghandoor, Chung-Ming Lo
Quantitative measurement of nanoscale surface roughness of articular cartilage tissue is significant to assess the surface topography for early treatment of osteoarthritis, the most common joint disease worldwide. Since it was not established by clinical diagnostic tools, the current studies have been suggesting the…
Philippe Boileau, Nima S. Hejazi, Sandrine Dudoit
Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering interpretable biological signal from data corrupted by technical noise remains a prevalent open problem. Several classes of procedures, among them classical dimensionality reduction…
Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot…
Authors not listed
Amines play a significant role in everyday life, and their detection remains a crucial focus in research and development. Although conducting polymer-based gas sensors have been widely reported for amine detection using DC resistivity measurements, they often lack selectivity to distinguish between intragroup…
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…
Authors not listed
Decades of extensive research have proved that β-amyloid (Aβ) peptides and their aggregation, inducing oxidative stress in the brain, play a key role in Alzheimer’s disease (AD) development. Moreover, Aβ peptides bind to Cu(II) ions, and the resulting complexes accelerate the aggregation process while promoting the…
Authors not listed
Lipidomics provides critical insights into disease mechanisms, biomarker discovery, and precision medicine, but conventional LC-MS workflows often require large sample volumes, limiting their application in minimally invasive studies. Here, we establish a nanoflow liquid chromatography (nanoLC) platform coupled with…
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…
Radu A. Talmazan, Jakob Gamper, Ivan Castillo, Thomas S. Hofer + 1 more
Supramolecular transition metal catalysts with tailored reaction environments allow for the usage of abundant 3d metals as catalytic centres, leading to more sustainable chemical processes. However, such catalysts are large and flexible systems with intricate interactions, resulting in complex reaction coordinates. To…