Similarity matrix
A similarity matrix is a matrix whose entries quantify pairwise similarity between objects, items, data points, or their representations.
Data Without Labels
An affinity matrix is a matrix whose entries quantify pairwise similarities or affinities between data points.
- How it is used
- Supports spectral clustering by encoding pairwise affinity for graph-based connectivity analysis.
- How deep it goes
- Defines similarity values and links them to spectral clustering, but stops short of detailing matrix construction algorithms.
- Examples
- Uses weighted graph diagrams, adjacency-matrix figures, complex cluster shapes, and an exercise deriving graph matrices.
- What you can do after
- Build an affinity representation from graph-based similarity weights and relate it to spectral clustering inputs.
Natural Language Processing in Action, Second Edition
A similarity matrix is a square matrix whose entries quantify pairwise similarity between objects.
- How it is used
- Computes pairwise relationships among sentence embeddings and supplies weights for graph-based connections.
- How deep it goes
- Defines dimensions, normalization, dot-product computation, symmetry, and graph conversion without developing matrix internals.
- Examples
- Uses chapter sentences, 300-dimensional embeddings, NumPy arrays, and a thresholded NetworkX graph.
- What you can do after
- Apply it via NumPy to calculate pairwise sentence similarity and derive weighted graph edges.
Read how Natural Language Processing in Action, Second Edition explains similarity matrix →
Graph Neural Networks in Action
A similarity matrix is a matrix whose entries quantify the similarity between pairs of items or representations.
- How it is used
- Builds product bundles by ranking items whose learned node embeddings are most similar.
- How deep it goes
- Explains matrix construction and nearest-item ranking, but defers the underlying cosine-similarity mathematics.
- Examples
- Uses Amazon product nodes and a Marceline vinyl figure to demonstrate six-item bundle generation.
- What you can do after
- Apply it via scikit-learn to rank products nearest to a chosen embedding.
Read how Graph Neural Networks in Action explains similarity matrix →
Related concepts
- spectral clusteringused by · Spectral clustering is a clustering method that partitions data using eigenvectors of a graph-based affinity representation rather than only geometric location.
- cosine similarityuses · Cosine similarity measures the directional alignment of two nonzero vectors as the cosine of the angle between them, equivalently their dot product divided by the product of their magnitudes.
- user-item interaction matrixused by · A user-item interaction matrix is a matrix whose rows represent users, columns represent items, and entries encode their interactions, preferences, or ratings.
- adjacency matrixalternative to · An adjacency matrix is a square matrix whose rows and columns represent graph vertices and whose entries indicate edges or encode edge weights between vertex pairs.
- embeddingproduced by · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
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