PRINCIPAL POLYNOMIAL ANALYSIS
نویسندگان
چکیده
منابع مشابه
Principal Polynomial Analysis
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. Contrarily to previous approaches, PPA reduces to performing simple univariate ...
متن کاملMyocardial Infarction Classification Using Polynomial Approximation and Principal Component Analysis
A rapid and accurate diagnosis in patients with acute myocardial infarction is vital, since expeditious reperfusion therapy can improve prognosis in most patients. Myocardial infarction occurred when the blood supply to part of the heart was interrupted. In ECG monitoring, ST segment means the change of electric potential in the period which from the end of ventricular depolarization to the ori...
متن کاملPersian 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 کاملPolynomial Singular Values for Number of Wideband Sources Estimation and Principal Component Analysis
A multipath enabled singular value decomposition (SVD) algorithm is presented, which will allow computation of wideband (polynomial) singular values, and hence, the signal+noise and noise subspaces. Polynomial singular values are ordered according to total energy. The number of sources can be estimated using the scalar total energy values. Results using both simulated data on the computer and a...
متن کاملMri Brain Image Classification Using Polynomial Kernel Principal Component Analysis with Neural Network
Magnetic Resonance (MR) Imaging has come up as widely accepted and revolutionary innovation in field of medical science and brain imaging especially. A new method is proposed here for MRI brain image classification using Polynomial Kernel Principle Component Analysis (KPCA) with Neural Network. In this paper, we are having various stages namely pre-processing, feature extraction, feature reduct...
متن کاملذخیره 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: International Journal of Neural Systems
سال: 2014
ISSN: 0129-0657,1793-6462
DOI: 10.1142/s0129065714400073