Dimensionality Reduction

Dimensionality reduction aims to transform high-dimensional data into lower-dimensional representations while preserving essential information, facilitating data visualization, analysis, and efficient processing. Current research emphasizes developing novel algorithms, including those based on neural networks (autoencoders, generative adversarial networks), graph neural networks, and adaptations of classical methods like PCA and t-SNE, to improve the accuracy and efficiency of dimensionality reduction for various data types (e.g., time series, images, graphs). These advancements are crucial for addressing the "curse of dimensionality" in diverse fields, ranging from medical image analysis and climate modeling to improving the performance and scalability of machine learning models.