Workshop: Optimising psychophysiological data quantification using Principal Components Analysis (PCA)

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

PCA vignette Principal components analysis with snpStats

Usually, principal components analysis is carried out by calculating the eigenvalues and eigenvectors of the correlation matrix. With N cases and P variables, if we write X for the N × P matrix which has been standardised so that columns have zero mean and unit standard deviation, we find the eigenvalues and eigenvectors of the P × P matrix X.X (which is N or (N − 1) times the correlation matri...

متن کامل

Principal components ancestry adjustment for Genetic Analysis Workshop 17 data

Statistical tests on rare variant data may well have type I error rates that differ from their nominal levels. Here, we use the Genetic Analysis Workshop 17 data to estimate type I error rates and powers of three models for identifying rare variants associated with a phenotype: (1) by using the number of minor alleles, age, and smoking status as predictor variables; (2) by using the number of m...

متن کامل

Persian Handwriting Analysis Using Functional Principal Components

Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...

متن کامل

"Spaghetti" PCA analysis: An extension of principal components analysis to time dependent interval data

In this paper a we present an extension of Principal Component Analysis to analyse time dependent interval data. Indeed, in our approach each observation is characterized by an oriented interval of values with a starting and an ending value for each period of observation: for example, the open and the close price of a share in a stock market for a day or a week, initial expression value and fin...

متن کامل

Approximations of the standard principal components analysis and kernel PCA

Principal component analysis (PCA) is a powerful technique for extracting structure from possibly highdimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to calculate eigenvalues and corresponding eigenvecto...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Frontiers in Human Neuroscience

سال: 2017

ISSN: 1662-5161

DOI: 10.3389/conf.fnhum.2017.224.00033