Search results for "Principal Component Analysis"

The on-line curvilinear component analysis (onCCA) for real-time data reduction

2015

Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component analysis, can work in real time by using iterative algorithms while all known nonlinear techniques cannot be implemented in such a way and actually always work on the whole database at each epoch. Among these nonlinear tools, the Curvilinear Component Analysis (CCA), which is a non-convex techni…

Dimensionality reduction via regression on hyperspectral infrared sounding data

2014

This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular…

Shelf life assessment of industrial durum wheat bread as a function of packaging system

2017

This study compared the effect of different packaging systems on industrial durum wheat bread shelf-life, with regard to thermoformed packaging (TF) and flow-packaging (FP). Two TFs having different thickness and one FP were compared by assessing physico-chemical and sensorial properties and volatile compounds of sliced bread during 90 days of storage. Texture, aw and bread moisture varied according to a first-order kinetic model, with FP samples ageing faster than TFs. Sensorial features such as consistency, stale odor, and sour odor, increased their intensity during storage. Furans decreased, whereas hexanal increased. The Principal Component Analysis of the whole dataset pointed out that…

New structural parameters of fullerenes for principal component analysis

2003

The Kekule structure count and the permanent of the adjacency matrix of fullerenes are related to structural parameters involving the presence of contiguous pentagons p, q, r, q/p and r/p, where p is the number of edges common to two pentagons, q is the number of vertices common to three pentagons and r is the number of pairs of nonadjacent pentagons adjacent to another common pentagon. The cluster analysis of the structural parameters allows classification these parameters. Principal component analysis (PCA) of the structural parameters and the cluster analyses of the fullerenes permit their classification. PCA clearly distinguishes five classes of fullerenes. The cluster analysis of fulle…

Table of periodic properties of human immunodeficiency virus inhibitors

2010

Classification algorithms are proposed based on information entropy. The feasibility of mixing a given human immunodeficiency virus (HIV) inhibitor with dissimilar ones is studied. The 31 inhibitors are classified by their structural chemical properties. Many classification algorithms are based on information entropy. An excessive number of results appear compatible with the data and suffer combinatorial explosion. However, after the equipartition conjecture one has a selection criterion. According to this conjecture, the best configuration is that in which entropy production is most uniformly distributed. The structural elements of an inhibitor can be ranked according to their inhibitory a…

Incremental Generalized Discriminative Common Vectors for Image Classification.

2015

Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without th…

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

2020

Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech.

2020

Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and te…

Sorting of Single Biomolecules based on Fourier Polar Representation of Surface Enhanced Raman Spectra

2016

AbstractSurface enhanced Raman scattering (SERS) spectroscopy becomes increasingly used in biosensors for its capacity to detect and identify single molecules. In practice, a large number of SERS spectra are acquired and reliable ranking methods are thus essential for analysing all these data. Supervised classification strategies, which are the most effective methods, are usually applied but they require pre-determined models or classes. In this work, we propose to sort SERS spectra in unknown groups with an alternative strategy called Fourier polar representation. This non-fitting method based on simple Fourier sine and cosine transforms produces a fast and graphical representation for sor…

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

2019

AbstractBackgroundPrincipal component analysis (PCA) is frequently useentirely written ind in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.ResultsWe developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny fra…