Journal ArticleDOI
Principal component analysis
TLDR
Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 1987-08-01. It has received 8660 citations till now. The article focuses on the topics: Principal component analysis & Multivariate statistics.read more
Citations
More filters
Journal ArticleDOI
A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
Journal ArticleDOI
A review of process fault detection and diagnosis: Part III: Process history based methods
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.
Journal ArticleDOI
Support Vector Machines for classification and regression
TL;DR: The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described, including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.
Journal ArticleDOI
Statistical process monitoring: basics and beyond
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.
Journal ArticleDOI
Computational Methods in Drug Discovery
TL;DR: Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades and theory behind the most important methods and recent successful applications are discussed.
References
More filters
Book
Applied Multivariate Statistical Analysis
R. A. Johnson,Dean W. Wichern +1 more
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
LIII. On lines and planes of closest fit to systems of points in space
TL;DR: 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.
Journal ArticleDOI
Applied Multivariate Statistical Analysis.
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
Partial least-squares regression: a tutorial
Paul Geladi,Bruce R. Kowalski +1 more
TL;DR: In this paper, a tutorial on the Partial Least Squares (PLS) regression method is provided, and an algorithm for a predictive PLS and some practical hints for its use are given.