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Term · Topic

Principal Component Analysis (PCA)

Principal component analysis is a statistical dimensionality reduction technique that converts correlated variables into uncorrelated principal components, enabling enterprises to simplify high-dimensional datasets, reduce computational load, and support more stable modeling and analytics in machine learning and business intelligence workflows.

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Vendors describe the same technology in different words. This record pins down what "PCA" means and ties it to the companies and coverage that use it, so an agent compares like with like.

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MCP

curl -X POST https://mcp.decisioninsights.ai/api/mcp-public \
  -H "content-type: application/json" \
  -H "accept: application/json, text/event-stream" \
  -d '{"id":1,"jsonrpc":"2.0","method":"tools/call","params":{"arguments":{"slug":"principal-component-analysis-pca"},"name":"di_get_term"}}'

REST

GET https://api.decisioninsights.ai/api/public/v1/glossary/f956af31-2f96-4297-832b-f3c35a7390f2

CLI

di get-glossary --slug principal-component-analysis-pca

JSON-LD

The schema.org markup this page publishes for search engines and AI agents, exactly as they read it.

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Is this your company? Get structured data for your own pages

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How an agent can rely on this definition

What the record lets an agent check before it uses the term in an answer.

The check What the record shows Where it is in the data
It's a known term One entry, with its abbreviation and type (topic) name, alternateName
It sits in a glossary Linked to Dimensionality Reduction, OLAP Cube, Predictive Analytics Dashboard, Feature Extraction inDefinedTermSet
View the raw record (JSON-LD)
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