Principal component analysis

  title={Principal component analysis},
  author={A. Hess and J. Hess},
P rincipal component analysis (PCA) is an old statistical technique for identifying major relationships in complex data. PCA consists of creating artificial variables (“components”) optimized to maximize how much variation is explained in a data set. It is widely used in exploratory data analysis and predictive modeling. Its uses can be as simple as deconvoluting spectra to determine the concentrations of the individual chemicals in a mixture or as complex as trying to define the elements of a… Expand
A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction
Experimental results show the proposed novel model outperforms the other comparison methods in terms of all evaluation indicators, and fuzzy clustering analysis can effectively improve the performance of the prediction model, and the proposed hybrid model can predict future dam deformation with high accuracy and efficiency. Expand
Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned
This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information. Expand
RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction
The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast Cancer and provides a theoretical basis for the intelligent diagnosis of Breast cancer. Expand
Multi-class EEG classification of motor imagery signal by finding optimal time segments and features using SNR-based mutual information
A new method was proposed to automatically find the best subject specific time intervals for the classification of four-class motor imagery tasks by using MI between the BCI input and output and the results suggested that the proposed method was efficient in classifying multi- class motor imagery signals as compared to other classification strategies proposed by the other studies. Expand
A regional suspended load yield estimation model for ungauged watersheds
Abstract Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield (SLY) in ungauged watersheds. However, using all theExpand
Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases.
Using only 1 μL of urine without enrichment or purification, polymer@Ag afforded urine metabolic fingerprints (UMFs) by LDI-MS in seconds and constructed a new diagnostic model to characterize subtypes of kidney diseases. Expand
Systems biology approaches to study lipidomes in health and disease.
This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications. Expand
Application of Artificial Intelligence in the Prediction of Thermal Properties of Biomass
This chapter highlights the methods, which have been applied in the prediction of the properties of biomass, and discusses the ANN-based prediction models for biomass as regards the thermal properties. Expand
Novel SOD2-enhancing therapies may delay the onset or reduce severity of HAND seen in ART-treated HIV-infected patients, suggesting the clinical relevance of these findings. Expand
Upregulation of Superoxide Dismutase 2 by Astrocytes in the SIV/Macaque Model of HIV-Associated Neurologic Disease.
These findings suggest that novel SOD2-enhancing therapies may reduce neuroinflammation in ART-treated HIV-infected patients and validate the clinical relevance of these data. Expand


A Tutorial on Principal Component Analysis
This manuscript focuses on building a solid intuition for how and why principal component analysis works, and crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. Expand
Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes
An overlooked but essential issue in assessing RBC unit quality and ultimately designing the necessary clinical trials is a metric for what constitutes an old or fresh RBC units. Expand
Metabolic fate of adenine in red blood cells during storage in SAGM solution
Red blood cells experience a three‐phase metabolic decay process during storage, resulting in the definition of three distinct metabolic phenotypes, occurring between Days 1 and 10, 11 and 17, and 18 and 46. Expand
Linear Algebra Done Right
-Preface for the Instructor-Preface for the Student-Acknowledgments-1. Vector Spaces- 2. Finite-Dimensional Vector Spaces- 3. Linear Maps- 4. Polynomials- 5. Eigenvalues, Eigenvectors, and InvariantExpand
LIII. On lines and planes of closest fit to systems of points in space
This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves. Expand
Linear Algebra and its Applications
Article history: Received 22 June 2010 Accepted 14 September 2010 Available online 12 October 2010 Submitted by R.A. Brualdi AMS classification: 11Y05 15A23
Blood groups in man. London: Thomas
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Dr. I. Drobnjak Oxford University MSc MMSC Signals Processing Lecture Notes
  • Dr. I. Drobnjak Oxford University MSc MMSC Signals Processing Lecture Notes
Gari Clifford MIT Blind Source Separation: PCA & ICA
  • Gari Clifford MIT Blind Source Separation: PCA & ICA
Jonathon Shlens A Tutorial on Principal Component Analysis
  • Jonathon Shlens A Tutorial on Principal Component Analysis