Types of Unsupervised Learning

Dimensionality Reduction

Dimensionality reduction is a technique used in unsupervised learning to reduce the number of input variables or features in a dataset. The goal of dimensionality reduction is to simplify the data without losing important information. There are several methods that can be used for dimensionality reduction, including:

• Principal Component Analysis (PCA): PCA is a popular technique for dimensionality reduction that involves finding linear combinations of the input variables that capture the most variance in the data. These linear combinations, called principal components, can be used to represent the data in a lower-dimensional space.
• t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear dimensionality reduction technique that is particularly well-suited for visualizing high-dimensional data in two or three dimensions. It works by modeling the similarities between data points in the high-dimensional space and then mapping them to a lower-dimensional space.
• Autoencoders: Autoencoders are neural networks that are trained to reconstruct the input data using a bottleneck layer that has fewer neurons than the input layer. By forcing the network to learn a compressed representation of the data, autoencoders can be used for dimensionality reduction.

Dimensionality reduction can be useful for a variety of applications, including visualization, data compression, and improving the performance of machine learning models. By reducing the number of input variables, dimensionality reduction can help to simplify the data and make it easier to analyze and interpret.

Overall, dimensionality reduction is an important tool in the unsupervised learning toolbox that can help to uncover hidden patterns and structures in data.