Unsupervised Metric

Unsupervised metric learning aims to develop methods for evaluating the similarity between data points without relying on labeled datasets, a crucial challenge across various machine learning domains. Current research focuses on developing novel metrics based on embedding ranks, piecewise-linear manifold approximations, and techniques leveraging clustering and dynamic time warping, particularly for applications in speech processing, image retrieval, and time series anomaly detection. These advancements are significant because they enable the evaluation and improvement of models in scenarios where labeled data is scarce or expensive to obtain, thereby expanding the applicability of machine learning to a wider range of real-world problems.