Sparse vector
A sparse vector is a vector in which most components are zero or otherwise absent.
Natural Language Processing in Action, Second Edition
A sparse vector is a vector in which most elements are zero or otherwise empty.
- How it is used
- Represents documents numerically for measuring distances and relationships in natural-language processing.
- How deep it goes
- Explains vocabulary-sized dimensions, mostly-zero entries, and search implications without developing sparse-storage internals.
- Examples
- Uses document word-count vectors, two-dimensional vectors [1, 0] and [2, 1], and a Python coordinate plot.
- What you can do after
- Evaluate document representations by relating vocabulary dimensions, zero entries, vector distances, and high-dimensional search costs.
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AI-Powered Search
A sparse vector is a vector representation in which most component values are zero or absent and only nonzero features are stored or processed.
- How it is used
- Supports efficient lexical retrieval, ranking, and sparse semantic query expansion in search pipelines.
- How deep it goes
- Defines representation, demonstrates cosine scoring, and explains inverted-index retrieval while deferring dense-vector techniques to separate treatment.
- Examples
- Uses an apple-juice cosine example, inverted-index retrieval, and SPLADE++ expansions for queries such as kimchi and Korean barbecue.
- What you can do after
- Apply sparse vectors through an inverted index and BM25, or inspect weighted sparse query expansions for ranking.
AI Agents in Action, Second Edition
A sparse vector is a vector in which most positions contain zero or otherwise absent values.
- How it is used
- Introduces sparse vectorization as an easy-to-follow starting point for document similarity search before semantic embeddings.
- How deep it goes
- Explains sparsity, TF-IDF mechanics, and vocabulary limits, but stops short of neural representation internals.
- Examples
- Uses a small document collection about skies, animals, and breakfasts, plus Python listings for vectorization and similarity search.
- What you can do after
- Apply sparse text vectorization via scikit-learn's TfidfVectorizer and compare document vectors with cosine similarity.
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Related concepts
- CountVectorizerproduced by · CountVectorizer is a scikit-learn transformer that converts text documents into a matrix of token-occurrence counts.
- embeddingcontrasts with · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- term frequency-inverse document frequencyproduced by · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.
- sparse representationpart of · A sparse representation stores non-default or present values and information locating them, while omitting or compressing default or absent values.
- vectorkind of · A vector is an ordered collection of components representing an element of a vector space.
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