[PDF] Top 20 MFPCA: Multiscale Functional Principal Component Analysis
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MFPCA: Multiscale Functional Principal Component Analysis
... Functional principal component analysis (FPCA) is a key tool for performing dimension reduction on functional data that features infinite dimensionality and emerges in many machine ... See full document
8
Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data
... Recently, functional principal component analysis (FPCA) has been explored as a statistical method for analysing wastewater data ...that functional data ana- lysis (FDA) is a reasonable ... See full document
12
Happ, Clara Maria (2017): Statistical methods for data with different dimensions: multivariate functional PCA and scalar-on-image regression. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... the functional case, by simply apply- ing them to the coefficient vectors and transforming the result back to the original ...e.g. principal component analysis (Ramsay and Silverman, 2005, ... See full document
226
Linguistic pitch analysis using functional principal component mixed effect models
... the functional data such as smoothing splines or ...first component represented “shift” and the second component represented “edge” ...this component in any case), it would be more ... See full document
30
Computational and Chemometrics study of molecular descriptors for butene derivates by Density functional theory (DFT)
... a principal component analysis (PCA) and hierarchical cluster analysis (HCA), have been employed with the aim of selecting the variables responsible for reactivity and to describe properly the ... See full document
11
Functional principal component analysis as a new methodology for the analysis of the impact of two rehabilitation protocols in functional recovery after stroke
... statistical analysis techniques. In this paper, traditional analysis (ANOVA) and FPCA were applied to study the functional recovery of 13 subjects who randomly partici- pated in two physiotherapy ... See full document
9
Automatic blood vessel segmentation in color images of retina
... steps: multiscale analysis using Gabor filters, feature extraction based on principal component analysis (PCA), and classifying the image pixels using their corresponding feature ... See full document
16
An Integrated Technique for Face Sketch Recognition Using DCNN
... and Multiscale Circular Weber Local Descriptor (MCWLD), Principal Component Analysis (PCA) is used for fusion of extracted features, DCNN used as a classifier to recognize the ... See full document
8
Examining the validity and reliability of the Activities Specific Balance Confidence Scale 6 (ABC 6) in a diverse group of older adults
... Cells of different genotypes and different ages have different sizes, so the perimeters are different. We test if perimeter could be a good classification parameter. The tools used to analyze the perimeter are a little ... See full document
64
Shape information from glucose curves: Functional data analysis compared with traditional summary measures
... traditional principal component analysis, FPCs may be interpreted and labelled according to the information they exhibit, which in turn can be related to more conventional physiological or clinical ... See full document
15
Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis
... Principal component analysis (PCA) has been widely used in many fields such as signal processing, image processing, pattern recognition and so on ...[8]. Principal component ... See full document
8
A Review of Constrained Principal Component Analysis (CPCA) with Application on Bootstrap
... regression analysis, where it was considered an important statistical development of the last fifty years, following general linear model (GLM), principal component analysis (PCA) and ... See full document
10
Sources Affecting PM2 5 Concentrations at a Rural Semi Arid Coastal Site in South Texas
... chemical analysis using instrumentation in- volves uncertainties or errors which are not considered in the PCA/APCS ...factor analysis model Positive Matrix Factorization 2 (PMF) based on least squares ap- ... See full document
11
Euler principal component analysis
... In pattern recognition, Principal Component Analysis (PCA) is perhaps the most classical tool for dimensionality reduc- tion and feature extraction. It is widely utilized in a great va- riety of ... See full document
21
Chemometric Feature Selection and Classification of Ganoderma lucidum Spores and Fruiting Body Using ATR FTIR Spectroscopy
... discriminant analysis (PDA) model was developed to identify informative spectral features for distinguishing between spores and fruiting body of ...using principal component discriminant ... See full document
11
A radical approach to handwritten Chinese character recognition using active handwriting models
... Ip et al. [5] applied snake fitting [7] to Chinese radical extraction with energy functional minimization. Their ex- periments were conducted on 36 character classes written by 10 people, and the initial results ... See full document
6
Face Recognition Using Principal Component Analysis
... using Principal Components Analysis based Genetic Algorithm in the area of computer vision is described in this ...image analysis plays an important role for human computer interaction but still now ... See full document
5
Principal Component Analysis of Volatility Smiles and Skews
... The empirical analysis has revealed two distinct regimes for short-term volatility in equity markets. In stable markets the range of the 1 month skew narrows as the index moves up and widens as the index moves ... See full document
16
Principal Component Analysis of the Volatility Smiles and Skews
... • Fengler, M., W. Hardle and C. Villa (2000) "The Dynamics of Implied Volatilities: A Common Principal Component Approach" Preliminary version (September 2000) available from ... See full document
20
A SURVEY: PRINCIPAL COMPONENT ANALYSIS (PCA)
... Principal Component Analysis is powerful statistical ...subsets. Principal Component Analysis are useful as data reduction but not for understanding the structure of the ... See full document
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