AI glossary · Machine learning
Dimensionality reduction
Dimensionality reduction simplifies data with many features into fewer, while keeping the most important information, so it is easier to analyse or visualise.
Also called PCA, principal component analysis
In more detail
Real data can have hundreds of measurements per item. Many of them overlap or add little. Techniques like principal component analysis (PCA) combine them into a few summary features.
It speeds up training, reduces noise and makes it possible to plot complex data, such as embeddings, on a 2D chart.
Related terms
Unsupervised learningUnsupervised learning finds patterns and groups in data that has no labels, such as discovering types of customers from their shopping habits.EmbeddingAn embedding is a list of numbers that represents the meaning of a piece of text, an image or another item, so a computer can measure how similar things are.ClusteringClustering is a machine learning technique that groups similar items together without being told the groups in advance.
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