Centroid Encoder

Centroid encoding is a technique that leverages class centroids—the average feature vectors representing each class—for various machine learning tasks. Research focuses on developing efficient and effective centroid-based models, including linear and non-linear architectures like sparse centroid encoders and adaptive bottleneck centroid encoders, often incorporating sparsity-inducing penalties for feature selection. These methods find applications in diverse areas such as feature selection, dimensionality reduction, and pre-training of deep learning models, improving performance and efficiency in tasks ranging from image classification to multi-document summarization. The resulting improvements in accuracy and computational efficiency are driving significant interest in centroid encoding across multiple domains.