Since 2020, aggregated from related topics
High-dimensional data refers to datasets that contain a large number of variables or dimensions compared to the number of observations. This type of data is common in fields such as genomics, neuroscience, finance, and social media analysis. Analyzing high-dimensional data poses unique challenges, as traditional statistical methods may not be suitable due to the "curse of dimensionality," where the number of variables increases exponentially with the number of observations. Researchers in the field of high-dimensional data aim to develop novel algorithms and techniques for data analysis, dimensionality reduction, feature selection, and visualization to extract meaningful insights from these complex datasets.