Principal component analysis or factor analysis different wording or methodological fault?
نویسنده
چکیده مقاله:
این مقاله چکیده ندارد
منابع مشابه
Principal component analysis and exploratory factor analysis.
In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful ...
متن کاملModeling multivariable hydrological series: Principal component analysis or independent component analysis?
[1] The generation of synthetic multivariate rainfall and/or streamflow time series that accurately simulate both the spatial and temporal dependence of the original multivariate series remains a challenging problem in hydrology and frequently requires either the estimation of a large number of model parameters or significant simplifying assumptions on the model structure. As an alternative, we...
متن کاملBiostatistics 302. Principal component and factor analysis.
Consider the situation where a researcher wants to determine the predictors for the fitness level (yes/no) to be assessed by treadmill by collecting the variables (Table I) of 50 subjects. Unfortunately the treadmill machine in the air-con room has broken down (the participants do not want to run in the hot sun!), and no assessment of fitness could be carried out. What could be done to analyse ...
متن کاملPrincipal Component Projection Without Principal Component Analysis
We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...
متن کاملA Comparison between Principal Component Analysis and Factor Analysis
The principal component analysis (also named Karhunen–Loève transformation) and the factor analysis are both tools of the multivariate statistics, more precisely the exploratory data analysis. They are used e.g. in data mining or machine learning. Although they share the same goal, they reach it with different methods. Over the years, some misunderstandings came up, how these methods differ fro...
متن کاملPrincipal Component, Independent Component and Parallel Factor Analysis
This talk is an introduction to Independent Component Analysis (ICA) and Parallel Factor Analysis (PARAFAC), the way they are related and their links with Principal Component Analysis (PCA). PCA is now a standard technique for the analysis of two-way multivariate data, i.e., data available in matrix format. However, principal components are subject to rotational in-variance. By imposing statist...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 15 شماره
صفحات 57- 58
تاریخ انتشار 2015-04
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
کلمات کلیدی برای این مقاله ارائه نشده است
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023