Probabilistic Linear Discriminant Analysis

Probabilistic Linear Discriminant Analysis (PLDA) is a statistical method used for classification, particularly excelling in scenarios with high dimensionality and limited labeled data. Current research focuses on improving PLDA's performance through techniques like covariance regularization, hierarchical modeling, and integration with other methods such as graph neural networks and deep learning embeddings, often addressing challenges in continual learning and extreme classification. These advancements enhance PLDA's applicability in diverse fields, including speaker recognition, image classification, and text classification, where its ability to handle uncertainty and adapt to new data is highly valuable.