Similarity Matrix

Similarity matrices represent pairwise relationships between data points, serving as fundamental inputs for various machine learning tasks like clustering, recommendation systems, and graph neural networks. Current research focuses on efficiently computing and approximating these matrices, particularly for high-dimensional data, employing techniques such as tensor decompositions, spectral methods, and graph neural networks to improve scalability and accuracy. These advancements are crucial for handling large datasets in diverse applications, ranging from image retrieval and video analysis to social network analysis and recommender systems, ultimately improving the performance and efficiency of numerous machine learning algorithms.