Description: High-dimensional space refers to a mathematical space that has a large number of dimensions, which can complicate the analysis and visualization of data. In this context, a dimension can be understood as a feature or attribute that describes an object or dataset. As the number of dimensions increases, the complexity of the relationships among the data also rises, making it difficult to identify patterns and interpret results. This phenomenon is known as the ‘curse of dimensionality,’ which suggests that in high-dimensional spaces, data tends to become sparse, making machine learning and statistical analysis algorithms less effective. Despite these challenges, high-dimensional space is fundamental in various fields such as artificial intelligence, machine learning, and statistics, where handling large volumes of data with multiple features is required. The ability to work in high-dimensional spaces allows researchers and data scientists to develop more complex and accurate models, although it also demands the use of advanced techniques for dimensionality reduction and data visualization.
