Affinity Matrix

An affinity matrix represents the pairwise similarity between data points, serving as a crucial input for various clustering algorithms, particularly spectral clustering. Current research focuses on improving affinity matrix construction through techniques like regularized projection approximations, sparsity-aware methods, and incorporating multi-view or multi-omics data. These advancements aim to enhance clustering accuracy, robustness to noise and outliers, and scalability to large datasets, impacting fields like speaker diarization, motion segmentation, and cancer subtype identification. The development of more efficient and accurate affinity matrices is vital for improving the performance of numerous machine learning applications.