Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
The focus of this article is to briefly introduce the idea of kernel methods and to implement a Gaussian radius basis function (RBF) kernel that is used to…
Machine Learning
Machine Learning
Predictive modeling, supervised machine learning, and pattern classification
When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of…
Machine Learning, python
Linear Discriminant Analysis
I received a lot of positive feedback about the step-wise Principal Component Analysis (PCA) implementation. Thus, I decided to write a little follow-up…
Uncategorised
Dixon’s Q test for outlier identification
I recently faced the impossible task to identify outliers in a dataset with very, very small sample sizes and Dixon’s Q test caught my attention. Honestly…
Machine Learning, python
About Feature Scaling and Normalization
I received a couple of questions in response to my previous article (Entry point: Data) where people asked me why I used Z-score standardization as feature…
Machine Learning & Statistics, programming
Clustering with Dirichlet Process Mixture Model in Java
In the previous articles we discussed in detail the Dirichlet Process Mixture Models and how they can be used in cluster analysis. In this article we will present a Java implementation of two different DPMM models: the Dirichlet Multivariate Normal Mix…
Machine Learning & Statistics
Clustering documents and gaussian data with Dirichlet Process Mixture Models
This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we wi…
Machine Learning, python
Entry Point Data
In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern…
Uncategorised
Molecular docking, estimating free energies of binding, and AutoDock’s semi-empirical force field
Discussions and questions about methods, approaches, and tools for estimating (relative) binding free energies of protein-ligand complexes are quite…
Machine Learning & Statistics
The Dirichlet Process Mixture Model
This blog post is the fourth part of the series on Clustering with Dirichlet Process Mixture Models. In previous articles we discussed the Finite Dirichlet Mixture Models and we took the limit of their model for infinite k clusters which led us to the …