Search results for "Principal component analysis"
showing 10 items of 486 documents
SDSS DR7 superclusters. Principal component analysis
2011
We apply the principal component analysis and Spearman's correlation test to study the properties of superclusters drawn from the SDSS DR7. We analyse possible selection effects in the supercluster catalogue, study the physical and morphological properties of superclusters, find their possible subsets, and determine scaling relations for superclusters. We show that the parameters of superclusters do not correlate with their distance. The correlations between the physical and morphological properties of superclusters are strong. Superclusters can be divided into two populations according to their total luminosity. High-luminosity superclusters form two sets, more elongated systems with the s…
Unsupervised deep feature extraction of hyperspectral images
2014
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms…
Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms.
2021
Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). …
Contribution to a Taxonomic Revision of the Sicilian Helichrysum Taxa by PCA Analysis of Their Essential-Oil Compositions
2016
The chemical profile of the essential oils in ten populations of the genus Helichrysum Mill. (Asteraceae), collected in the loci classici of the nomenclatural types of the taxa endemic to Sicily, were analyzed. Our results confirm that the analysis of secondary metabolites can be used to fingerprint wild populations of Helichrysum, the chemical profiles being coherent with the systematic arrangement of the investigated populations in three main clusters, referring to the aggregates of H. stoechas, H. rupestre, and H. italicum, all belonging to the section Stoechadina. The correct nomenclatural designation of the investigated populations is discussed and the following two new combinations ar…
Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants
2014
Chromatographic retention times of phenylurea herbicides are modelled by structure–property relationships. Properties are hydration free energy and dipole. Bioplastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among evolutionary indeterminacy, morphological determination and natural selection principles. Classification algorithms are proposed based on information entropy and production. Phenylureas are classified by Cl2, O2 and N2 presence; their different behaviour depends on the number of Cl atoms. When applying procedures to moderate-sized sets, excessive results appear compatible with data and suffer a combinatorial e…
Multivariate analysis of historical data (2004-2013) in assessing the possible environmental impact of the Bellolampo landfill (Palermo).
2017
Multivariate analysis was performed on a large data set of groundwater and leachate samples collected during 9 years of operation of the Bellolampo municipal solid waste landfill (located above Palermo, Italy). The aim was to obtain the most likely correlations among the data. The analysis results are presented. Groundwater samples were collected in the period 2004–2013, whereas the leachate analysis refers to the period 2006–2013. For groundwater, statistical data evaluation revealed notable differences among the samples taken from the numerous wells located around the landfill. Characteristic parameters revealed by principal component analysis (PCA) were more deeply investigated, and corr…
Distribution of Heavy Metals in Marine Sediments of Palermo Gulf (Sicily, Italy)
2008
Concentrations of Cr, Cu, Hg, Pb and Zn have been measured, by atomic absorption spectrophotometry, in the fine fraction (<63 μm) of surface sediments collected in 30 sites in the Palermo Gulf (Sicily, Italy) in order to assess the levels and the spatial distribution of these elements. Enrichment factors calculated with respect to clean areas have been considered to discriminate between levels due to background or to pollution contributions. The sampling stations, which form a grid inside these areas, are characterized by geographic proximity and by the presence of pollution sources. Ratio matching technique along with hierarchical clustering, minimum spanning tree and principal component a…
Principal polynomial analysis for remote sensing data processing
2011
Inspired by the concept of Principal Curves, in this paper, we define Principal Polynomials as a non-linear generalization of Principal Components to overcome the conditional mean independence restriction of PCA. Principal Polynomials deform the straight Principal Components by minimizing the regression error (or variance) in the corresponding orthogonal subspaces. We propose to use a projection on a series of these polynomials to set a new nonlinear data representation: the Principal Polynomial Analysis (PPA). We prove that the dimensionality reduction error in PPA is always lower than in PCA. Lower truncation error and increased independence suggest that unsupervised PPA features can be b…
Minimal learning machine in hyperspectral imaging classification
2020
A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum is classified pixel-by-pixel. In some of the real-time applications, the amount of the HS image data causes performance challenges. Those issues relate to the platforms (e.g. drones) payload restrictions, the issues of the available energy and to the complexity of the machine learning models. In this study, we introduce the minimal learning machine (MLM) as a computationally cheap training and classification machine learning method for the hyperspectral imaging classificatio…
Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data
2010
The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI-TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre-processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to o…