Dimensionality reduction

As explained in Delivering Machine Learning Projects: From design to deployment

Dimensionality reduction is the transformation of data into fewer dimensions while attempting to preserve informative structure.

Dimensionality reduction transforms high-dimensional embeddings into fewer dimensions so teams can visualize distribution and inspect structure indirectly. PCA and t-SNE can help, but may lose substantial information. section 6.1.4

Use it as an exploratory work item:

• Generate embeddings for unstructured items with a suitable foundational model. section 6.1.4
• Apply PCA or t-SNE to visualize the embedding distribution, then record what the projection reveals and what information it may discard. section 6.1.4
• For richer exploration, index embeddings with FAISS or Annoy and query nearest neighbors instead of relying only on a projection. section 6.1.4

Shakespeare play exploration

Split plays into sentences, embed them with all-MiniLM-L12-v2, index them with FAISS, and visualize nearest-neighbor links as a heatmap. The resulting relationships can suggest similarities or distances between plays, but still require interpretation. section 6.1.4

Figure 6.4 Heat map of nearest neighbor connections in Shakespeare’s plays using all-MiniLM-L12-v2

Use the heatmap to inspect nearest-neighbor connections among Shakespeare’s plays. section 6.1.4

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes

Explains the visualization workflow and information-loss risk, but does not develop the underlying algorithms mathematically.

Examples

Uses embedded Shakespeare sentences, FAISS neighbor queries, and heatmaps comparing plays.

What it assumes

Assumes access to generated embeddings and teaches how to inspect them indirectly rather than consume their coordinates directly.

What you can do after

Evaluate unstructured-data structure by projecting embeddings or querying their nearest neighbors.

How it connects

Connects embeddings with PCA, t-SNE, vector indexing, nearest-neighbor search, and heatmaps.

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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIDimensionality reduction transforms data with many variables into a lower-dimensional representation while attempting to preserve important structure or information.
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  Cyber Threat HuntingDimensionality reduction transforms data into a representation with fewer features while retaining important information.
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  Inside Deep Learning: Math, Algorithms, ModelsThe process of mapping data from a higher-dimensional space into a lower-dimensional representation while retaining important structure.

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Related concepts

• principal component analysisincludes · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.
• UMAPincludes · UMAP, or Uniform Manifold Approximation and Projection, is a nonlinear manifold-learning technique that embeds high-dimensional data in a lower-dimensional space while preserving important neighborhood and structural relationships.
• t-SNEincludes · t-SNE is a nonlinear dimensionality-reduction technique that maps high-dimensional data to a lower-dimensional space while emphasizing local similarities or neighborhood relationships.
• curse of dimensionalityaddresses · The curse of dimensionality is the set of statistical, geometric, and computational difficulties that tend to increase as the number of dimensions grows.
• embeddinguses · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• unsupervised learningkind of · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.