Vertices Principal Component Analysis (V-PCA) and Centers Principal Component Analysis (C-PCA) are variants of Principal Component Analysis (PCA) to deal with two-way interval-valued data. In this case the observation units are represented as hyperrectangles instead of points. Tucker3 and CANDECOMP/PARAFAC are component analysis techniques to analyze the underlying structure of three-way data sets. In the present paper, after recalling the above mentioned methods, we extend the C-PCA and V-PCA methods to deal with three-way interval-valued data by means of Tucker3 and CANDECOMP/PARAFAC and we describe how to represent the observation units in the obtained low-dimensional space. Furthermore, an application of the extended methods—called Thre...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
We introduce a special type of interval description depending on time. Each observation is character...
Vertices Principal Component Analysis (V-PCA) and Centers Principal Component Analysis (C-PCA) are v...
International audienceOne feature of contemporary datasets is that instead of the single point value...
Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis (C-PCA) gene...
Real world data analysis is often affected by different types of errors as: measurement errors, comp...
One feature of contemporary datasets is that instead of the single point value in the p-dimensional ...
The present paper deals with the study of continuous interval data by means of suitable Principal Co...
Principal Component Analysis (PCA) is a linear data analysis tool that aims to reduce the dimensiona...
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for exa...
For the exploratory analysis of three-way data, the Tucker3 is one of the most applied models to stu...
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for exa...
Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge ...
<p>Crosses, triangles and circles represent variables assigned to each of the dimensions. X and Y ax...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
We introduce a special type of interval description depending on time. Each observation is character...
Vertices Principal Component Analysis (V-PCA) and Centers Principal Component Analysis (C-PCA) are v...
International audienceOne feature of contemporary datasets is that instead of the single point value...
Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis (C-PCA) gene...
Real world data analysis is often affected by different types of errors as: measurement errors, comp...
One feature of contemporary datasets is that instead of the single point value in the p-dimensional ...
The present paper deals with the study of continuous interval data by means of suitable Principal Co...
Principal Component Analysis (PCA) is a linear data analysis tool that aims to reduce the dimensiona...
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for exa...
For the exploratory analysis of three-way data, the Tucker3 is one of the most applied models to stu...
Three-way component analysis techniques are designed for descriptive analysis of 3-way data, for exa...
Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge ...
<p>Crosses, triangles and circles represent variables assigned to each of the dimensions. X and Y ax...
<p>A, B and C: Hyperplane between the first and second principal components (PC1 and PC2, respective...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
We introduce a special type of interval description depending on time. Each observation is character...