15,039 research outputs found
Unsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the
past decade. Traditional unsupervised methods select the features which can
faithfully preserve the intrinsic structures of data, where the intrinsic
structures are estimated using all the input features of data. However, the
estimated intrinsic structures are unreliable/inaccurate when the redundant and
noisy features are not removed. Therefore, we face a dilemma here: one need the
true structures of data to identify the informative features, and one need the
informative features to accurately estimate the true structures of data. To
address this, we propose a unified learning framework which performs structure
learning and feature selection simultaneously. The structures are adaptively
learned from the results of feature selection, and the informative features are
reselected to preserve the refined structures of data. By leveraging the
interactions between these two essential tasks, we are able to capture accurate
structures and select more informative features. Experimental results on many
benchmark data sets demonstrate that the proposed method outperforms many state
of the art unsupervised feature selection methods
Machine learning for crystal identification and discovery
As computers get faster, researchers -- not hardware or algorithms -- become
the bottleneck in scientific discovery. Computational study of colloidal
self-assembly is one area that is keenly affected: even after computers
generate massive amounts of raw data, performing an exhaustive search to
determine what (if any) ordered structures occur in a large parameter space of
many simulations can be excruciating. We demonstrate how machine learning can
be applied to discover interesting areas of parameter space in colloidal self
assembly. We create numerical fingerprints -- inspired by bond orientational
order diagrams -- of structures found in self-assembly studies and use these
descriptors to both find interesting regions in a phase diagram and identify
characteristic local environments in simulations in an automated manner for
simple and complex crystal structures. Utilizing these methods allows analysis
methods to keep up with the data generation ability of modern high-throughput
computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary
informatio
Identification of body fat tissues in MRI data
In recent years non-invasive medical diagnostic techniques have been used widely in medical investigations. Among the various imaging modalities available, Magnetic Resonance Imaging is very attractive as it produces multi-slice images where the contrast between various types of body tissues such as muscle, ligaments and fat is well defined. The aim of this paper is to describe the implementation of an unsupervised image analysis algorithm able to identify the body fat tissues from a sequence of MR images encoded in DICOM format. The developed algorithm consists of three main steps. The first step pre-processes the MR images in order to reduce the level of noise. The second step extracts the image areas representing fat tissues by using an unsupervised clustering algorithm. Finally, image refinements are applied to reclassify the pixels adjacent to the initial fat estimate and to eliminate outliers. The experimental data indicates that the proposed implementation returns accurate results and furthermore is robust to noise and to greyscale in-homogeneity
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.Comment: ACM CCS 201
- âŠ