HDIM

HDIM stands for Hybrid Dimension. It is a relative term used in various fields, but primarily in the realms of data representation and dimensionality reduction. The concept introduces a hybrid approach to representing data, combining two or more dimensionality reduction techniques or feature selection methods.

In the context of data science, HDIM can refer to the cumulative effect of multiple feature selection techniques or dimensionality reduction algorithms applied to a dataset. For instance, applying principle component analysis (PCA) and k-means clustering to a dataset would result in a hybrid dimensional space. This space can exhibit improved data compression, noise reduction, and pattern extraction compared to using a single technique.

HDIM can also be interpreted as a combination of different feature sets or attribute combinations in multi-dimensional analysis. For instance, in social network analysis, where a user's profile characteristics, behavior metrics, and network statistics are used to define different multidimensional aspects of the user. Combining these aspects through HDIM may provide a richer representation of the user, amplifying the discovery of user patterns and trends.

Researchers primarily use HDIM in domains like computer vision, medical imaging analysis, and social network analysis to achieve better results in data analysis, pattern recognition, and information extraction. By coupling multiple techniques, HDIM strips out multi-variance in feature/dimension combinations that might not individually show salience in data interpretation.