# Unsupervised Classification of Hyperspectral Images using Latent Dirichlet Allocation

## Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a type of probabilistic topic model commonly used in natural language processing to extract topics from large collections of documents in an unsupervised manner. LDA assumes that each document in a corpus (collection of documents) is associated with a mixture of ...

# Whitening Characteristics of the Mahalanobis Distance

Mahalanobis distance is a metric used to compare a vector to a multivariate normal distribution with a given mean vector ($\boldsymbol{\mu}$) and covariance matrix ($\boldsymbol{\Sigma}$). It is often used to detect statistical outliers (e.g., in the RX anomaly detector) and also appears in the exponential term of ...

# Visualizing Dirichlet Distributions with Matplotlib

This post describes how I went about visualizing probability density functions of 3-dimensional Dirichlet distributions with matplotlib. If you're already familiar with the Dirichlet distribution, you might want to skip the next section.

## Rolling Dice

To understand what the Dirichlet distribution describes, it is useful to consider how it ...

# Anomalously Non-Anomalous Anomaly Detection Results

## The RX Anomaly Detector

In image processing, anomaly detectors are algorithms used to detect image pixels that are sufficiently different than other pixels in the same image (or within a local neighborhood of the pixel being evaluated). The RX anomaly detector [1] represents each pixel in an image as a ...