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Simplicial Nonlinear Principal Component Analysis [PDF]
, 2012 We present a new manifold learning algorithm that takes a set of data points lying on or near a lower dimensional manifold as input, possibly with noise, and outputs a simplicial complex that fits the data and the manifold.
Hunt, Thomas, Krener, Arthur J.
core +1 more source
FEBS Letters, EarlyView.Knowing how proteases recognise preferred substrates facilitates matching proteases to applications. The S1′ pocket of protease EA1 directs cleavage to the N‐terminal side of hydrophobic residues, particularly leucine. The S1′ pocket of thermolysin differs from EA's at only one position (leucine in place of phenylalanine), which decreases cleavage ...
Grant R. Broomfield +3 more
wiley +1 more source
Low-Light Image Enhancement by Principal Component Analysis
IEEE Access, 2019 Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components.
Steffi Agino Priyanka +2 more
doaj +1 more source
Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis
Remote Sensing, 2020 Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent ...
Alina L. Machidon +4 more
doaj +1 more source
Data Exploration Using Tableau and Principal Component Analysis
JOIV: International Journal on Informatics Visualization, 2022 This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling.
Hanna Arini Parhusip +4 more
doaj +1 more source
Interactive Principal Component Analysis [PDF]
2017 21st International Conference Information Visualisation (IV), 2017 Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and ...
Siirtola Harri +2 more
openaire +4 more sources
FEBS Letters, EarlyView.The Arabidopsis mutants hls1 hlh1 and amp1 lamp1 exhibit pleiotropic developmental phenotypes. Although the functions of the causative genes remain unclear, they act in the same genetic pathway and are thought to generate non‐cell‐autonomous signals.
Takashi Nobusawa, Makoto Kusaba
wiley +1 more source
Jurnal Ilmu Dasar, 2010 Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data.
Ismail Djakaria +2 more
doaj
A principal component analysis for trees
The Annals of Applied Statistics, 2009 The active field of Functional Data Analysis (about understanding the variation in a set of curves) has been recently extended to Object Oriented Data Analysis, which considers populations of more general objects. A particularly challenging extension of this set of ideas is to populations of tree-structured objects.
Aydın, Burcu +4 more
openaire +4 more sources
FEBS Letters, EarlyView.Spinal muscular atrophy (SMA) is a genetic disease affecting motor neurons. Individuals with SMA experience mitochondrial dysfunction and oxidative stress. The aim of the study was to investigate the effect of an antioxidant and neuroprotective substance, ergothioneine (ERGO), on an SMNΔ7 mouse model of SMA.
Francesca Cadile +8 more
wiley +1 more source