Add to ORKGA new type of discriminant space for functional data is presented, com-bining the advantages of a functional discriminant coordinate space and a functional principal component space. In order to provide a comprehen-sive comparison, we conducted a set of experiments, testing effectiveness on 35 functional data sets (time series). Experiments show that constructed combined space provides a higher quality of classification of LDA method compared with component spaces
Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction ...
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. ...
Abstract- In this paper, a novel simple dimension reduction technique for classification is proposed...
Analyzing functional data often leads to finding common factors, for which functional principal comp...
Linear discriminant analysis is studied when the predictors are data of functional type(curves). Due...
With the advance of modern technology, more and more data are being recorded continuously during a t...
Most of existing methods of functional data classification deal with one or a few processes. In this...
In functional principal component analysis (PCA), we treat the data that consist of functions not of...
<div><p>Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality re...
This thesis delves into the world of Functional Data Analysis (FDA) and its analog of Principal Comp...
Advances in data collection and storage have tremendously increased the presence of functional data,...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Factor analysis and discriminant analysis are often used as complementary approaches to identify lin...
In this paper, we will present a unified view for LDA. We will (1) emphasize that standard LDA solut...
We address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such func...
Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction ...
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. ...
Abstract- In this paper, a novel simple dimension reduction technique for classification is proposed...
Analyzing functional data often leads to finding common factors, for which functional principal comp...
Linear discriminant analysis is studied when the predictors are data of functional type(curves). Due...
With the advance of modern technology, more and more data are being recorded continuously during a t...
Most of existing methods of functional data classification deal with one or a few processes. In this...
In functional principal component analysis (PCA), we treat the data that consist of functions not of...
<div><p>Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality re...
This thesis delves into the world of Functional Data Analysis (FDA) and its analog of Principal Comp...
Advances in data collection and storage have tremendously increased the presence of functional data,...
Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techn...
Factor analysis and discriminant analysis are often used as complementary approaches to identify lin...
In this paper, we will present a unified view for LDA. We will (1) emphasize that standard LDA solut...
We address the problem of dimension reduction for time series of functional data (Xt:t∈Z). Such func...
Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction ...
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. ...
Abstract- In this paper, a novel simple dimension reduction technique for classification is proposed...