搜索结果: 1-15 共查到“principal component”相关记录53条 . 查询时间(0.109 秒)
Network Degradation Assessed by Evolved Gas Analysis-Mass Spectrometry Combined with Principal Component Analysis (EGA-MS-PCA): A Case of Thermo-Oxidized Epoxy/Amine Network
ULTRAVIOLET-IRRADIATION MECHANICAL-PROPERTIES BUTADIENE RUBBER EPOXY-RESINS TEMPERATURE PYROLYSIS COATINGS TRANSITION OXIDATION SCISSION
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2023/11/16
To study the degradation of thermosetting polymers, we apply a novel method to simultaneously study the chemical structural changes and network topology: evolved gas analysis-mass spectrometry combine...
HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS
Hyperspectral Images Noise Reduction Nonlocal Similarity Spectral Spatial Information Principal Component Analysis
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2018/5/14
Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classi...
AUTOMATIC CLASSIFICATION METHODS OF HIGH-RESOLUTION SATELLITE IMAGES:THE PRINCIPAL COMPONENT ANALYSIS APPLIED TO THE SAMPLE TRAINING SET
Remote Sensing IKONOS Classification Land Cover Processing Georeferencing
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2015/12/31
In Remote Sensing the various bands of multispectral data have not the same relevance in order to identify pixels inside a specific land cover class. The band algebra combines different images in orde...
DETECTION AND INTERPRETATION OF GEOLOGICAL LINEAR FEATURES ON THE SATELLITE IMAGES BY USING GRADIENT FILTERING AND PRINCIPAL COMPONENT ANALYSIS
Geological Linear Feature Edge Detections Filtering Remote Sensing Landsat
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2015/12/29
Digital image processing has the ability to detect geological linear features on the image by using some algorithms. The most common algorithm which is used for this purpose is edge filtering. Gradien...
Improving and Extending the Information on Principal Component Analysis for Local Neighborhoods in 3D Point Clouds
Laser Scanning Point clouds Curvature Approximation
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2015/12/16
Principal Component Analysis (PCA) is often utilised in point cloud processing as provides an efficient method to approximate local
point properties through the examination of the local neighbourhood...
Improving Satellite Quickbird-Based Identification Of Landscape Archaeological Features Through Principal Component Analysis And Tasseled Cap Transformation
Remote Sensing Archaeology QuickBird Satellite High resolution
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2015/11/12
In this paper, the Tasseled Cap Transformation (TCT) was applied to QuickBird multispectral images for extracting archaeological features linked to ancient human transformations of the landscape. The ...
DETECTION OF HOTSPOTS IN NOAA/AVHRR IMAGES USING PRINCIPAL COMPONENT ANALYSIS AND INFORMATION FUSION TECHNIQUE
Hotspots NOAA/AVHRR Satellite Imaging
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2015/8/28
India accounts for the world’s greatest concentration of coal fires which cause several devastating environmental effects. Only Jharia
Coal Field (JCF) in Jharkhand (India) contains nearly half of su...
Potential reasons for ionospheric anomalies detected by nonlinear principal component analysis just before the China Wenchuan earthquake, and their relationship to source conditions
Nonlinear Principal Component Analysis (NLPCA) Principal Component Analysis (PCA) Total Electron Content (TEC),
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2015/8/27
Nonlinear principal component analysis (NLPCA) was performed to examine the total electron content (TEC) anomalies for the China Wenchuan earthquake of May 12, 2008 (= 7.9). This was applied to global...
Ionospheric perturbations associated with two huge earthquakes in Japan, using principal component analysis for multiple subionospheric VLF/LF propagation paths
Ionospheric perturbations Earthquakes Subionospheric VLF/LF propagation
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2015/8/24
The presence of ionospheric perturbations in possible association with two huge earthquakes (Noto-hanto peninsula and Niigata-chuetu-oki earthquakes) in 2007 was studied on the basis of a conventional...
Principal component models for sparse functional data
Functional data analysis Principal components Mixed effects model Reduced rank estimation Growth curve
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2015/8/21
The elements of a multivariate data set are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete m...
Sparse Principal Component Analysis
Arrays Gene expression Lasso/elastic net Multivariate analysis Singular value decomposition Thresholding
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2015/8/21
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
MULTIVARIATE MATHEMATICAL MORPHOLOGY BASED ON PRINCIPAL COMPONENT ANALYSIS: INITIAL RESULTS IN BUILDING EXTRACTION
Multichannel image processing colour morphology vector ordering principal component analysis urban analysis
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2015/7/30
Today, colour or multichannel satellite and aerial images are increasingly becoming available due to the commercial availability of
multispectral digital sensors and pansharpening function of the co...
Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit
Dense Error Correction Low-Rank Matrices Principal Component Pursuit
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2015/6/17
We consider the problem of recovering a lowrank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been sh...
Stable Principal Component Pursuit
Principal Component Pursuit
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2015/6/17
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross sparse errors. Recently,...
Robust Principal Component Analysis?
Principal components robustness vis-a-vis outliers nuclear-norm minimization `1-norm minimization duality low-rank matrices sparsity video surveillance
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2015/6/17
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove...