Unsupervised Learning

Anomaly Detection using Gaussian Mixtures

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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 normal instances are called inliers. Anomaly detection is very useful in a wide variety of applications, for example in fraud detection, or for detecting defective … Read more

Understanding DBSCAN Clustering Algorithm: Implementation in Python

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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 approach allows the algorithm to identify clusters of arbitrary shapes. Understanding DBSCAN Clustering Algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular … Read more

Using Clustering for Semi-Supervised Learning

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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 of 50 labeled instances from the digits dataset: n_labeled = 50 log_reg = LogisticRegression() log_reg.fit(X_train[:n_labeled], y_train[:n_labeled]) What is the performance of this model on the … Read more

Using clustering for image segmentation

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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 segment. For example, in a self-driving car’s vision system, all pixels that are part of a pedestrian’s image might be assigned to the “pedestrian” segment … Read more

Implementation of K-Means Clustering in Machine Learning

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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 very quickly and efficiently, often in just a few iterations. It was proposed by Stuart Lloyd at the Bell Labs in 1957 as a technique … Read more

Gaussian Mixtures Models in Machine Learning

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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 generated from a single Gaussian distribution form a cluster that typically looks like an ellipsoid. Each cluster can have a different ellipsoidal shape, size, density … Read more