Unsupervised Learning

In this blog, we will describe unsupervised learning in more detail, and enlist several popular unsupervised learning algorithms.

Introduction of Unsupervised Learning

The second family of machine learning algorithms that we will discuss is unsupervised learning.

Unsupervised learning includes all kinds of machine learning where there is no known output, no teacher to instruct the learning algorithm. In unsupervised learning, the learning algorithm is just shown the input data and asked to extract knowledge from this data.

Types of unsupervised learning

There are 2 kinds of unsupervised machine learning :

  1. Unsupervised transformations

  2. Clustering

Unsupervised transformations of a dataset are algorithms that create a new representation of the data which might be easier for humans or other machine learning algorithms to understand.

A common application of unsupervised transformations is dimensionality reduction, which takes a high-dimensional representation of the data, consisting of many features, and finding a new way to represent this data that summarizes the essential characteristics about the data with fewer features. A common application for dimenofsionality reduction is a reduction to two dimensions for visualization purposes.dimensionality.

Clustering algorithms on the other hand partition data into distinct groups of similar items.

Consider the example of uploading photos to a social media site. To allow you to organize your pictures, the site might want to group together pictures that show the same person. However, the site doesn’t know which pictures show whom, and it

doesn’t know how many different people appear in your photo collection. A sensible approach would be to extract all faces, and divide them into groups of faces that look similar. Hopefully, these correspond to the same person, and can be grouped together or you.

Unsupervised Machine learning Algorithms :

  1. PCA (principal component analysis)

  2. K-means clustering

  3. Agglomerative Clustering


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