We present a tutorial survey on some recent approaches to unsupervised machine learning in the context of statistical pattern recognition. In statistical PR, there are two classical categories for unsupervised learning methods and models: first, variations of Principal Component Analysis and Factor Analysis, and second, learning vector coding or clustering methods. These are the starting-point ...

An important challenge in the field of unsupervised learning is not only the development of algorithms that infer model parameters given some dataset but also to implement them in a way so that they can be applied to problems of realistic size and to su ciently complex benchmark problems. We developed a lightweight, easy to use MPI (Massage Passing Interface) based Python framework that can be ...

Graph construction is a crucial step in spectral clustering (SC) and graph-based semi-supervised learning (SSL). Spectral methods applied on standard graphs such as full-RBF, ǫ-graphs and k-NN graphs can lead to poor performance in the presence of proximal and unbalanced data. This is because spectral methods based on minimizing RatioCut or normalized cut on these graphs tend to put more import...

This paper discusses (based on the EU project AQUA) how the core elements of three complementary approaches and standards can be integrated into one compact skill set with training and best practices to be applied. In this project experts from Automotive SPICE (ISO 15504), Functional Safety (ISO 26262) and Lean Six Sigma collaborate. In a first analysis the experts identified an architecture of...

A new spiking-neural-network model for partitioning data into clusters has been developed. The learning process is based on the Spike Timing-Dependent Plasticity rule under the Hebbian Learning framework. With temporally encoded inputs, the synaptic efficiencies of the delays between the pre and postsynaptic spikes can store the information of different data clusters. Various simulation results...

Angle-resolved photoelectron spectroscopy of the unpaired electron in sodium-doped water, methanol, ammonia, and dimethyl ether clusters is presented. The experimental observations and the complementary calculations are consistent with surface electrons for the cluster size range studied. Evidence against internally solvated electrons is provided by the photoelectron angular distribution. The t...

We generalize the ordinary aggregation process to allow for choice. In ordinary aggregation, two random clusters merge and form a larger aggregate. In our implementation of choice, a target cluster and two candidate clusters are randomly selected and the target cluster merges with the larger of the two candidate clusters. We study the long-time asymptotic behavior and find that as in ordinary a...

The paper illustrates a linguistic knowledge acquisition model making use of data types, innite memory, and an inferential mechanism for inducing new information from known data. The model is compared with standard stochastic methods applied to data tokens, and tested on a task of lexico{semantic classi cation.

Traditionally, multitask learning (MTL) assumes that all the tasks are related. This can lead to negative transfer when tasks are indeed incoherent. Recently, a number of approaches have been proposed that alleviate this problem by discovering the underlying task clusters or relationships. However, they are limited to modeling these relationships at the task level, which may be restrictive in s...

We describe the ensemble X-ray properties of high redshift clusters with emphasis on changes with respect to the local population. Cluster X-ray luminosity evolution is detected in five nearly independent surveys. The relevant issue now is characterizing this evolution. Cluster temperature evolution provides constraints on the dark matter and dark energy content of the universe. These constrain...