Robustifying multiple-set linear canonical analysis with S-estimator
نویسندگان
چکیده
منابع مشابه
A unified approach to multiple-set canonical correlation analysis and principal components analysis.
Multiple-set canonical correlation analysis and principal components analysis are popular data reduction techniques in various fields, including psychology. Both techniques aim to extract a series of weighted composites or components of observed variables for the purpose of data reduction. However, their objectives of performing data reduction are different. Multiple-set canonical correlation a...
متن کاملA New Ridge Estimator in Linear Measurement Error Model with Stochastic Linear Restrictions
In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...
متن کاملRobustifying Eeg Data Analysis by Removing Outliers
Biomedical signals such as EEG are typically contaminated by measurement artifacts, outliers and non-standard noise sources. We propose to use techniques from robust statistics and machine learning to reduce the influence of such distortions. Two showcase application scenarios are studied: (a) Lateralized Readiness Potential (LRP) analysis, where we show that a robust treatment of the EEG allow...
متن کاملFunctional Linear Regression Via Canonical Analysis
We study regression models for the situation where both dependent and independent variables are square integrable stochastic processes. Questions concerning definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical component...
متن کاملMultiple Attribute Learning with Canonical Correlation Analysis and Em Algorithm
This paper presents a new framework of learning pattern recognition, called \multiple attribute learning". In usual setting of pattern recognition, target patterns have several attribute such as color, size, shape, and we can classify the patterns in several ways by their color, by their size, or by their shape. in normal pattern recognition problem, an attribute or a mode of classi cation is c...
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: Comptes Rendus. Mathématique
سال: 2020
ISSN: 1778-3569
DOI: 10.5802/crmath.74