Sparse representation
A sparse representation stores non-default or present values and information locating them, while omitting or compressing default or absent values.
Natural Language Processing in Action, Second Edition
A sparse representation stores only nonzero or otherwise present values instead of explicitly storing the many default values in a larger representation.
- How it is used
- Represents high-dimensional document features compactly for counting, TF–IDF calculation, similarity, and search.
- How deep it goes
- Explains omitted zeros, compressed storage, CSR output, mathematical operations, and the memory cost of densification.
- Examples
- Uses CountVectorizer outputs, n-gram matrices, algorithmic-bias queries, and Hacker Public Radio TF–IDF search.
- What you can do after
- Apply it via scikit-learn to build, inspect, and search sparse count or TF–IDF document matrices.
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Graph Neural Networks in Action
SparseTensor is the torch_sparse sparse multidimensional tensor structure that stores values and indices rather than a full dense array.
- How it is used
- Reduces memory requirements when storing and training on graphs whose adjacency matrices contain relatively few nonzero entries.
- How deep it goes
- Defines sparse storage, compares it with dense storage, and demonstrates two PyG conversion routes without detailing sparse operation internals.
- Examples
- Uses PyG’s ToSparseTensor dataset transform and SparseTensor.from_dense with a dense adjacency matrix.
- What you can do after
- Apply it via PyTorch Geometric or torch_sparse to convert dense graph data into a memory-efficient sparse representation.
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Deep Learning with JAX
A tensor representation that stores only non-zero values to reduce memory usage.
- How it is used
- Reduces memory usage for tensors containing many zeros by storing only non-zero values.
- How deep it goes
- Defines sparse storage and its tradeoff, but defers the experimental API and implementation details.
- Examples
- Illustrates tensors containing many zero-valued elements and comparatively few non-zero values.
- What you can do after
- Evaluate when sparse storage could save memory and recognize the need for sparsity-aware operations.
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Starting Data Analytics with Generative AI and Python
A sparse matrix is a matrix data structure that stores primarily nonzero elements without allocating equivalent storage for the many zero elements.
- How it is used
- Supports memory-conscious co-occurrence analysis for matrices dominated by zero word-pair counts.
- How deep it goes
- Defines sparse storage and demonstrates lil_matrix construction, but defers broader optimization issues.
- Examples
- Uses word co-occurrence matrices and a visualization of the 30 most common co-occurring pairs.
- What you can do after
- Apply it via SciPy's sparse package to reduce storage demands in zero-heavy co-occurrence matrices.
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Related concepts
- CountVectorizerused by · CountVectorizer is a scikit-learn transformer that converts text documents into a matrix of token-occurrence counts.
- arraykind of · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
- dense matrixcontrasts with · A dense matrix is a matrix representation that explicitly stores a value for every position, including positions containing zeros.
- pandasused by · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
- dense representationalternative to · A dense representation explicitly stores values for most or all dimensions or positions, including zero or otherwise uninformative values.
- scikit-learnused by · scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
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