Embeddings
In machine learning, embeddings refer to the representation of discrete variables—such as words, items, or users—as continuous vectors in a lower-dimensional semantic space. These dense vector representations are learned from data such that items with similar meanings or contexts are located close to each other in the vector space, capturing underlying semantic relationships. Embeddings solve the problem of high-dimensional, sparse representations (like one-hot encoding) by providing a compact format that neural networks can process efficiently. They are a critical component in natural language processing, recommendation systems, and any application requiring similarity search.
Key Concepts
- Dense Vectors: Unlike sparse vectors which contain mostly zeros, embeddings are dense, meaning most elements in the vector have non-zero continuous values, efficiently capturing rich semantic information.
- Semantic Similarity: The geometric distance or angle (such as cosine similarity) between two embedding vectors reflects the semantic similarity between the concepts they represent.
- Word Embeddings: Popularized by algorithms like Word2Vec and GloVe, these techniques map words or phrases to vectors, ensuring that words used in similar contexts have similar representations.
- Dimensionality Reduction: The process of taking high-dimensional categorical data and projecting it down to a more manageable lower-dimensional space while preserving essential relationships.