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2025-06-10T06:20:30Z

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	dimensionality reduction machine learning, what is dimensionality reduction, PCA in machine learning, reduce features in data, data visualization techniques, t-SNE, autoencoder, high-dimensional data analysis		dimensionality reduction machine learning, what is dimensionality reduction, PCA in machine learning, reduce features in data, data visualization techniques, t-SNE, autoencoder, high-dimensional data analysis
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Dimensionality Reduction = '''Dimensionality Reduction''' is a technique in machine learning and data analysis used to reduce the number of input variables (features) while preserving as much relevant information as possible. == Why Use Dimensionality Reduction? == High-dimensional data can lead to problems such as: * '''Overfitting:''' Too many features can cause the model to learn noise. * '''Increased Computation:''' More features = more time and resources...."

2025-06-10T06:11:26Z

Created page with "= Dimensionality Reduction = '''Dimensionality Reduction''' is a technique in machine learning and data analysis used to reduce the number of input variables (features) while preserving as much relevant information as possible. == Why Use Dimensionality Reduction? == High-dimensional data can lead to problems such as: * '''Overfitting:''' Too many features can cause the model to learn noise. * '''Increased Computation:''' More features = more time and resources...."

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= Dimensionality Reduction =

'''Dimensionality Reduction''' is a technique in machine learning and data analysis used to reduce the number of input variables (features) while preserving as much relevant information as possible.

== Why Use Dimensionality Reduction? ==

High-dimensional data can lead to problems such as:

* '''Overfitting:''' Too many features can cause the model to learn noise.
* '''Increased Computation:''' More features = more time and resources.
* '''Curse of Dimensionality:''' As dimensions increase, data becomes sparse, making patterns harder to detect.
* '''Poor Visualization:''' Hard to visualize data beyond 3 dimensions.

Dimensionality reduction simplifies the dataset, improving model performance and interpretability.

== Common Techniques ==

=== 1. Principal Component Analysis (PCA) ===

* Transforms original features into a smaller number of uncorrelated variables (principal components).
* Captures the directions of maximum variance in the data.

=== 2. Linear Discriminant Analysis (LDA) ===

* Supervised technique that reduces dimensions while maximizing class separability.

=== 3. t-Distributed Stochastic Neighbor Embedding (t-SNE) ===

* Non-linear technique mainly used for visualizing high-dimensional data in 2D or 3D.

=== 4. Autoencoders ===

* Neural networks that learn efficient codings of input data in unsupervised manner.

== Example ==

Suppose a dataset has 100 features. PCA can reduce it to 10 or 20 principal components that still retain most of the information, making it easier to process and visualize.

== Applications of Dimensionality Reduction ==

* Preprocessing step before clustering or classification
* Noise reduction
* Data visualization
* Feature selection and extraction
* Bioinformatics and image processing

== Challenges ==

* Risk of losing important information
* Interpretation of transformed features can be difficult
* Choice of method depends on the data and goal

== Related Pages ==

* [[Unsupervised Learning]]
* [[Principal Component Analysis (PCA)]]
* [[t-SNE]]
* [[Autoencoder]]
* [[Feature Selection]]
* [[Clustering]]

== SEO Keywords ==

dimensionality reduction machine learning, what is dimensionality reduction, PCA in machine learning, reduce features in data, data visualization techniques, t-SNE, autoencoder, high-dimensional data analysis

Dimensionality Reduction - Revision history

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