CORRECTION OF SYSTEMATIC RADIOMETRIC INHOMOGENEITY IN SCANNED AERIAL CAMPAIGNS USING PRINCIPAL COMPONENT ANALYSIS

Correction: Correction: Genetic Evaluation of Dual-Purpose Buffaloes (Bubalus bubalis) in Colombia Using Principal Component Analysis

Internal Traits of Eggs and Their Relationship to Shank Feathering in Chicken Using Principal Component Analysis

Chicken eggs represent an important source of protein to the growing human population and also supply repositories of unique genes that could be used worldwide. The inheritance of shank feathering trait is dominant upon non-feathering shank trait in chicken which is based on two factors: pti-1L and pti-1B that are located on Chromosomes 13, 15, and 24. Using 185 fertile eggs collected from two ...

demonstrating buried channels using principal component analysis

spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

Multiresolution using principal component analysis

This paper proposes Principal Component Analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l2 energy. With only minor modification, a single layer linear network using the Generalized Hebbian Algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully app...

Principal Component Analysis using R

This tutorial is designed to give the reader a short overview of Principal Component Analysis (PCA) using R. PCA is a useful statistical method that has found application in a variety of fields and is a common technique for finding patterns in data of high dimension. Consider we are confronted with the following situation: The data, we want to work with, are in form of a matrix (xij)i=1...N,j=1...