Density Estimation in High-Dimensional Spaces: A Multivariate Histogram Approach

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Pedro Strecht (Author) REIT; João Mendes-Moreira (Author) FEUP; Carlos Soares (Author) FEUP

Conference proceedings International

Title: ADVANCED DATA MINING AND APPLICATIONS, ADMA 2022, PT II

Pages: 266-278

18th International Conference on Advanced Data Mining and Applications (ADMA)

Brisbane, AUSTRALIA, NOV 28-30, 2022

Indexing

ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: P-00X-FZ9

DOI: 10.1007/978-3-031-22137-8_20

Abstract (EN): Density estimation is an important tool for data analysis. Non-parametric approaches have a reputation for offering state-of-the-art density estimates limited to few dimensions. Despite providing less accurate density estimates, histogram-based approaches remain the only alternative for datasets in high-dimensional spaces. In this paper, we present a multivariate histogram approach to estimate the density of a dataset without restrictions on the number of dimensions, containing both numerical and categorical variables (without numerical encoding) and allowing missing data (without the need to preprocess them). Results from the empirical evaluation show that it is possible to estimate the density of datasets without restrictions on dimensionality, and the method is robust to missing values and categorical variables.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13