Multi Stage Clustering

Multi-stage clustering is a machine learning technique that refines clustering results through iterative stages, often incorporating different algorithms or feature representations at each step. Current research focuses on improving the accuracy and efficiency of these methods, particularly addressing challenges like noisy data, overconfident pseudo-labels, and representation collapse through techniques such as self-training, self-distillation, and novel objective functions designed to mitigate these issues. These advancements are impacting various fields, including computer vision (landmark discovery, image clustering), signal processing (speaker diarization), and data mining (feature selection), by enabling more robust and efficient unsupervised learning approaches. The development of generalized multi-stage clustering frameworks that adapt to diverse data types and resource constraints is a key area of ongoing investigation.