Anomaly Detection using Gaussian Mixtures
Introduction Anomaly detection (also called outlier detection) is the task of detecting instances that deviate strongly from the norm. These instances are of course called anomalies or outliers, while the…
Understanding DBSCAN Clustering Algorithm: Implementation in Python
Before we move on to Gaussian mixture models, let’s take a look at DBSCAN, another popular clustering algorithm that illustrates a very different approach based on local density estimation. This…
Using Clustering for Semi-Supervised Learning
Another use case for clustering is in semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances. Let’s train a logistic regression model on a sample…
Using clustering for image segmentation
Image segmentation is the task of partitioning an image into multiple segments. In semantic segmentation, all pixels that are part of the same object type get assigned to the same…
Implementation of K-Means Clustering in Machine Learning
Consider the unlabeled dataset represented in Figure below: you can clearly see 5 blobs of instances. The K-Means algorithm is a simple algorithm capable of clustering this kind of dataset…
What is Clustering? A Simple Approach
As you enjoy a hike in the mountains, you stumble upon a plant you have never seen before. You look around and you notice a few more. They are not…
Gaussian Mixtures Models in Machine Learning
A Gaussian mixture model (GMM) is a probabilistic model that assumes that the instances were generated from a mixture of several Gaussian distributions whose parameters are unknown. All the instances…
Introduction to Unsupervised Learning Techniques
Introduction Although most of the applications of Machine Learning today are based on supervised learning (and as a result, this is where most of the investments go to), the vast…