Optimal Projection

Optimal projection techniques aim to find the best low-dimensional representation of high-dimensional data, maximizing information relevance for specific tasks like classification or rendering. Current research focuses on developing novel projection methods, including those based on Johnson-Lindenstrauss lemma, Wasserstein distance variations (e.g., Sliced Wasserstein), and generalized kernel approaches like incomplete gamma kernels, often incorporating self-attention mechanisms for improved efficiency and performance. These advancements improve the accuracy and efficiency of various applications, ranging from image processing and point cloud reconstruction to machine learning classification tasks.