Topic
Principal component analysis
About: Principal component analysis is a research topic. Over the lifetime, 22148 publications have been published within this topic receiving 691657 citations. The topic is also known as: PCA & principal components analysis.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the vector estimate is presented in a special quadratic form aimed to improve the error of estimation compared with traditional linear estimates, and the vector is first pre-estimated from the special iterative procedure such that each iterative loop consists of a solution of the unconstrained nonlinear best approximation problem.
8 citations
01 Jan 2016
8 citations
Journal Article•
27 May 2012-World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering
TL;DR: In this paper, principal component analysis is applied to reduce the number of original variables and a model based on the first two principal components accounts for 72.24% of total variance.
Abstract: The objective of this research is to study principa l component analysis for classification of 67 soil sa mples collected from different agricultural areas in the western part of Thailand. Six soil properties were measured on the soil samples and ar e used as original variables. Principal component analysis is applied to reduce the number of original variables. A model based on the first two principal components accounts for 72.24% of total v ariance. Score plots of first two principal components were used t o map with agricultural areas divided into horticulture, field crops and wetland. The results showed some relationships between soil pr perties and agricultural areas. PCA was shown to be a useful to ol for agricultural areas classification based on soil properties. Keywords—soil organic matter, soil properties, classificatio n, principal components
8 citations
22 Aug 2007
TL;DR: A new method for detecting poultry skin tumors in hyperspectral reflectance images using the principal component analysis, discrete wavelet transform, and kernel discriminant analysis is presented.
Abstract: This paper presents a new method for detecting poultry skin tumors in hyperspectral reflectance images. We employ the principal component analysis (PCA), discrete wavelet transform (DWT), and kernel discriminant analysis (KDA) to extract the independent feature sets in hyperspectral reflectance image data. These features are individually classified by a linear classifier and their classification results are combined using product rule. The final classification result based on the proposed method shows the better performance in detecting tumors compared with previous works.
8 citations
15 Sep 2005
Abstract: A combination of gene ranking, dimensional reduction, and recursive feature elimination (RFE) using a BP-MLP artificial neural network (ANN) was used to select genes for DNA microarray classification. Use of k-means cluster analysis for dimensional reduction and maximum sensitivity for RFE resulted in 64-gene models with fewer invariant and correlated features when compared with PCA and mimimum error. In conclusion, k-means cluster analysis and sensitivity may be better suited for classifying diseases for which gene expression is more strongly influenced by pathway heterogeneity.
8 citations