Object Classification via PCANet and Color Constancy Model

2014 ◽  
Vol 635-637 ◽  
pp. 997-1000 ◽  
Author(s):  
De Kun Hu ◽  
Li Zhang ◽  
Wei Dong Zhao ◽  
Tao Yan
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Functional Properties ◽  
Principle Component Analysis ◽  
Color Constancy ◽  
Principal Component ◽  
Object Classification ◽  
Component Analysis ◽  
Color Images ◽  
Principle Component

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals Support vector regression is an improvement for principle component analysis

2021 ◽  
Vol 23 (06) ◽  
pp. 1699-1715
Author(s):  
Mohamed, A. M. ◽  
◽  
Abdel Latif, S. H ◽  
Alwan, A. S. ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principle Component Analysis ◽  
Mean Squared Error ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Functions ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Principle Component

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

Principle Component Analysis Based on New Symmetric Similarity Measures for Heavy-Tailed Data

2018 ◽  
Vol 17 (04) ◽  
pp. 1850029
Author(s):  
Mohammad Seidpisheh ◽  
Adel Mohammadpour
Keyword(s):  
Principal Component Analysis ◽  
Similarity Measure ◽  
Principle Component Analysis ◽  
Similarity Measures ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Artificial Datasets

We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.

Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis

2000 ◽  
Vol 92 (6) ◽  
pp. 1545-1552 ◽  
Author(s):  
Petra Bischoff ◽  
Eckehard Scharein ◽  
Gunter N. Schmidt ◽  
Georg von Knobelsdorff ◽  
Burkhart Bromm ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Frequency Band ◽  
Principle Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Fourier Transformation ◽  
Complex Data ◽  
Multivariate Statistical ◽  
Double Blind ◽  
Principle Component

Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

Comparative study of the physicochemical, compositional and functional properties of eight flours obtained from different plant materials found in Colombia

2016 ◽  
Vol 22 (8) ◽  
pp. 699-707 ◽  
Author(s):  
Seneida Lopera-Cardona ◽  
Cecilia Gallardo ◽  
Jairo Umaña-Gallego ◽  
Lina María Gil
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Ananas Comosus ◽  
Whole Grain ◽  
Plant Materials ◽  
Pineapple Peel ◽  
Yellow Peas ◽  
Oyster Mushrooms

The physicochemical, compositional and functional properties of flour from green plantains ( Musa acuminata) of the large green plantain variety, oyster mushrooms ( Pleorotus ostreatus), pineapple peel ( Ananas comosus) of the ‘apple pineapple’ variety, yellow peas ( Pisum sativum), chickpeas ( Cicer arietinum), whole grain rice ( Oryza sativa), whole grain corn ( Zea mays) and whole grain white quinoa (Chenopodium quinoa) were evaluated by using one-way analysis of variance, Pearson correlations and principal component analysis chemical composition of the eight flours, statistically differed ( p < 0.05). Oyster mushroom and yellow pea flours had the greatest protein content (28.92 and 21.02%, respectively), whereas the pineapple peel, peas and corn stood out for their high contents of Fe and Zn. All flours exhibited emulsifying and foaming activities, while hydration and interfacial properties showed statistically significant negative correlations. There was a clear relationship between levels of protein and carbohydrates and gelation and syneresis phenomena in thermally treated flour suspensions. According to principal component analysis of functional, physicochemical and compositional properties, flours were classified into five groups of raw materials: (1) yellow peas, (2) chickpeas, rice, corn and quinoa, (3) green plantain, (4) pineapple peel and (5) oyster mushrooms. Results are promising to formulate mixes and composite flours for fortification and/or enrichment of food products by using different technological processes.

scholarly journals Physicochemical and Techno-Functional Characterization of Native Corn Reintroduced in the Andean Zone of Jujuy, Argentina

Proceedings ◽  
2020 ◽  
Vol 53 (1) ◽  
pp. 7
Author(s):  
María Alejandra Giménez ◽  
Cristina Noemí Segundo ◽  
Manuel Oscar Lobo ◽  
Norma Cristina Sammán
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Food Industry ◽  
Functional Characterization ◽  
Principal Component ◽  
Component Analysis ◽  
High Variability ◽  
Pca Analysis

The chemical and techno-functional properties of nine maize races from the Andean zone of Jujuy, Argentina, in the process of reintroduction, were determined. Principal component analysis (PCA) was applied to establish the differences between them. The breeds studied showed high variability in their chemical and techno-functional properties, which would indicate that their applications in the food industry will also be differentiated. The PCA analysis allowed us to group them into four groups, and the Capia Marron and Culli races showed unique properties, mainly in the formation of gels.

Raman imaging and principal component analysis-based data processing on uranium oxide ceramics

2017 ◽  
Vol 129 ◽  
pp. 260-269 ◽  
Author(s):  
O.A. Maslova ◽  
G. Guimbretière ◽  
M.R. Ammar ◽  
L. Desgranges ◽  
C. Jégou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Uranium Oxide ◽  
Principal Component ◽  
Component Analysis ◽  
Raman Imaging ◽  
Oxide Ceramics

scholarly journals Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

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