embeddingsrepresentation
Embeddingsrepresentation is the concept of representing discrete or high-dimensional data items as dense, continuous vectors in a shared embedding space. The goal is to encode semantic, syntactic, or relational patterns so that similar items are nearby in the space. This approach underpins many machine learning tasks by providing compact, differentiable representations that can be fed into models and refined during training.
Common forms include word embeddings, sentence embeddings, document embeddings, and graph or node embeddings. Word embeddings
Learning methods range from classical count-based approaches like latent semantic analysis and matrix factorization to predictive
Applications span information retrieval, semantic search, clustering, recommendation, transfer learning, and knowledge graph completion. Challenges include