Feature vector

As explained in Graph Algorithms for Data Science

A feature vector is a vector whose numerical components encode selected characteristics of a data point or node.

A feature vector is a numerical representation of selected characteristics belonging to a data point or node. For user analysis, extracted feature vectors support comparisons between users. section 8.0

Use feature vectors to turn extracted characteristics into relationships between users:

• Extract features from each user and represent them as a vector. section 8.0
• Compare pairs of vectors with cosine similarity. section 8.0
• Construct a similarity network whose connections join users with similar features. section 8.0
• Apply community detection to identify user segments from densely connected groups. section 8.0

Figure 8.1 Extracting node representations and constructing a similarity network based on them

Shows how node representations become a similarity network. section 8.0

How Graph Algorithms for Data Science covers it

How deep it goes

Explains vector use through cosine similarity and network construction, without detailing feature-extraction internals.

Examples

Uses Twitter users, extracted user characteristics, pairwise cosine similarity, and inferred user segments.

What it assumes

Assumes familiarity with representing selected characteristics numerically and comparing nodes in a network.

What you can do after

Apply it via cosine similarity to construct user-similarity networks and support community-based segmentation.

How it connects

Connects feature extraction, cosine similarity, similarity networks, and community detection.

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Also explained in

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  Build a Text-to-Image Generator (from Scratch)A feature vector is a numerical vector whose components encode properties of an input or intermediate representation.

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This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.