High-Dimensional Data Analysis and Visualisation
Conference
Category: International Association for Statistical Computing (IASC)
Proposal Description
In the field of dimension reduction, the session focuses on methods for analysing high-dimensional data in both supervised and unsupervised settings, with particular emphasis on complex data structures such as matrices, tensors, and text data involving categorical, quantitative, or mixed-type variables. Topics of interest include clustering and classification methods, correspondence analysis, biplots, and related multivariate techniques for the exploration and interpretation of complex data. Special attention is also given to the visualisation of high-dimensional and complex data structures.
Submissions
- Clustering and multidimensional scaling of asymmetric dissimilarities with external information
- Convex Clustering of Mixed Numerical and Categorical Data
- From Continuous Handwriting Measurements to Categorical Association Maps Using Multiple Correspondence Analysis
- On the Growth of Correspondence Analysis in Indonesia: A Bibliography
- The Generalised Singular Value Decomposition in High-Dimensional Multivariate Methods.