Self Expressive

Self-expressive models aim to represent data points as linear combinations of others within their dataset, facilitating unsupervised clustering and dimensionality reduction, particularly in subspace clustering. Current research focuses on improving the efficiency and effectiveness of these models, exploring deep learning architectures like autoencoders and novel algorithms that leverage contrastive learning and data augmentation to enhance representation learning and address computational limitations associated with large datasets. These advancements are significant for various applications, including knowledge graph embedding, motion capture analysis, and general data clustering tasks, by enabling more accurate and scalable analysis of complex datasets.