ISDE : Independence Structure Density Estimation
This work addresses density estimation challenges for high-dimensional data, but appears incremental as it builds on existing independence structure models.
The paper tackles the curse of dimensionality in multivariate density estimation by proposing ISDE, an algorithm that separates features into independent groups under the Independence Structure model, and shows its performance through experiments on synthetic and real-world data with comparisons of empirical log-likelihood and variable partitions.
In this paper, we propose ISDE (Independence Structure Density Estimation), an algorithm designed to estimate a multivariate density under Kullback-Leibler loss and the Independence Structure (IS) model. IS tackles the curse of dimensionality by separating features into independent groups. We explain the construction of ISDE and present some experiments to show its performance on synthetic and real-world data. Performance is measured quantitatively by comparing empirical $\log$-likelihood with other density estimation methods and qualitatively by analyzing outputted partitions of variables. We also provide information about complexity and running time.