Identifying structural changes with unsupervised machine learning methods

Kernel Methods for Unsupervised Learning Kernel Methods for Unsupervised Learning Title: Kernel Methods for Unsupervised Learning

Kernel Methods are algorithms that projects input data by a nonlinear mapping in a new space (Feature Space). In this thesis we have investigated Kernel Methods for Unsupervised learning, namely Kernel Methods that do not require targeted data. Two classical unsupervised learning problems using Kernel Methods have been tackled. The former is the Data Dimensionality Estimation, the latter is the...

Unsupervised Machine Learning

Identifying structural flow defects in disordered solids using machine-learning methods.

We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temper...

Machine Learning for NLP: Unsupervised learning techniques

• So far we have seen supervised learning (of classification): – learning based on a training set where labelling of instances represents the target (categorisation) function – classifier implements an approximation of the target funtion – outcome: a classification decision • Unsupervised learning: – learning based on unannotated instances; – outcome: a grouping of objects (instances and groups...

Identifying Uncertain Galaxy Morphologies Using Unsupervised Learning

With the onset of massive cosmological data collection through mediums such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are, in...