Dimensionality reduction

As explained in Grokking Machine Learning

A technique that simplifies data by reducing or combining features while preserving as much information as possible.

Dimensionality reduction simplifies data by combining similar features and reducing the number of columns while losing as little information as possible. It is an unsupervised-learning technique that can also preprocess data for supervised learning. section 2.5

Figure 2.7 Dimensionality reduction algorithms help us simplify our data. On the left, we have a housing dataset with many features. We can use dimensionality reduction to reduce the number of features in the dataset without losing much information and obtain the dataset on the right.

A housing dataset with many features becomes simpler by using fewer features while retaining much of its information. section 2.3

Dimensionality reduction and clustering simplify data in different ways:

• Dimensionality reduction reduces columns, while clustering can reduce rows by grouping data points. section 2.3

How Grokking Machine Learning covers it

How deep it goes

Defines feature reduction and illustrates fewer columns, but does not detail an algorithm.

Examples

Uses a housing dataset with many features to illustrate reducing features while retaining much of the information.

What you can do after

Understand how reducing columns can simplify a dataset while retaining much of its information.

How it connects

Connects dimensionality reduction to unsupervised learning, preprocessing, clustering, matrix factorization, and singular value decomposition.

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Also explained in

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  Data Without LabelsDimensionality reduction is the transformation of data into fewer dimensions while retaining important structure or information.
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  Math and Architectures of Deep LearningThe reduction of the effective number of dimensions in a representation by a transformation such as a singular weight matrix.
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  Natural Language Processing in Action, Second EditionDimension reduction is the transformation of data into a representation with fewer features or dimensions while retaining relevant information.
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  Outlier Detection in PythonDimensionality reduction is the transformation of data into fewer dimensions while retaining relevant information.
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  Graph Neural Networks in ActionDimensionality reduction is the transformation of data into a representation with fewer numerical dimensions while retaining selected information or structure.
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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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  Graph Algorithms for Data ScienceDimensionality reduction is a technique that represents data with fewer numerical dimensions while preserving selected structure or information.
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  Delivering Machine Learning Projects: From design to deploymentDimensionality reduction is the transformation of data into fewer dimensions while attempting to preserve informative structure.
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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.

Compare how 11 books explain dimensionality reduction →

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 Grokking Machine Learning. The section links point to the parts of the book it draws on.