Abbreviations ChIP Chromatin immunoprecipitation EST Expressed sequence tag ORF Open reading frame PCA Principle component analysis SAGE Serial analysis of gene expression SOM Self-organizing map SVM Support vector machine

Principle component analysis (PCA) is commonly used to compute a bounding box of a point set in R. In this paper we give bounds on the approximation factor of PCA bounding boxes of convex polygons in R 2 (lower and upper bounds) and convex polyhedra in R (lower bound).

We describe a compression scheme for the geometry component of 3D animation sequences. This scheme is based on the principle component analysis (PCA) method, which represents the animation sequence using a small number of basis functions. Second-order linear prediction coding (LPC) is applied to the PCA coefficients in order to further reduce the code size by exploiting the temporal coherence p...

the aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of ‎ gene expression data using artificial neural networks. the studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in durham in 2009. of all the studied patients, 66 patients were suffering ...

an ideal fusion method preserves the spectral information in fused image without spatial distortion. the pca is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. however, it can distort the spectral characteristics of multispectral images. the current paper tries to present a new fusion method based on the same concept. in ...

Active Shape Model (ASM) is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis (PCA) to statistically model the variability in the training set of example shapes. Independent Component Analysis (ICA) has been proven to be more efficient to extract face features than PCA . In this paper, we combine the P...

Principle Component Analysis (PCA) is a mathematical procedure widely used in exploratory data analysis, signal processing, etc. However, it is often considered a black box operation whose results and procedures are difficult to understand. The goal of this paper is to provide a detailed explanation of PCA based on a designed visual analytics tool that visualizes the results of principal compon...

1.3. Statistical analysis & Expert systems in toxicological screening: 1-9 1.3.1. CLOUDS-Overlap 1-10 1.3.2. SMART scaling 1-12 1.3.3. Commonly used terms in statistical pattern recognition 1-12 1.3.3.1. Cross-validation 1-12 1.3.3.2. Classification vs. regression 1-12 1.3.3.3. Supervised vs. unsupervised methods 1-13 1.3.4. Pattern recognition techniques 1-13 1.3.5. Principle component analysi...

since the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, instead of an ensemble of an n–d realization. although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to better results. in this paper, first a method is proposed to extract th...