### What is dimensionality reduction?

Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.

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Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.

Principal Component Analysis (PCA) is a dimension reduction technique.

Kernel PCA extends regular PCA to situations where linear transformations are not satisfactory in capturing the variability within the data.

ICA is a specialized dimensionality reduction technique that is used for finding independent components within a multivariate signal.

Factor Analysis is a dimensionality reduction technique that, like PCA, attempts to explain the variability across a set of features

T-SNE is best suited for visualizing high-dimensional data in only two dimensions.

PCA is a linear dimensionality reduction technique while T-SNE is a non-linear technique

Random Projection is a dimensionality reduction technique that maps observations from higher dimensional space into lower dimensional space

PCA is preferable to Random Projection.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
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- Distance Measures (9)
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- Deep Learning (23)
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