80,962 research outputs found
Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation
A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex
RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short time scales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex
Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation
A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results
Self-organizing map based adaptive sampling
We propose a new adaptive sampling method that uses Self-Organizing Maps (SOM). In SOM, densely sampled regions in the input space is represented by a larger area on the map than that of sparsely sampled regions. We use this property to progressively tune-in on the interesting region of the design space. The method does not rely on parameterized distribution, and can sample from multi-modal and non-convex distributions. In this paper, we minimize several mathematical test functions. We also show its performance in inequality-constrained objective satisfaction problem, in which the objective is to seek diversity in solutions satisfying certain upper-bound constraint in the minimized objective. A new merit function and a measure of space-filling quality were proposed for this purpose
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
In the recent years, there is a growing interest in semi-supervised learning,
since, in many learning tasks, there is a plentiful supply of unlabeled data,
but insufficient labeled ones. Hence, Semi-Supervised learning models can
benefit from both types of data to improve the obtained performance. Also, it
is important to develop methods that are easy to parameterize in a way that is
robust to the different characteristics of the data at hand. This article
presents a new method based on Self-Organizing Map (SOM) for clustering and
classification, called Adaptive Local Thresholds Semi-Supervised
Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of
learning at training time, according to the availability of labels, as in
previous models, and can automatically adjust itself to the local variance
observed in each data cluster. The results show that the ALTSS-SOM surpass the
performance of other semi-supervised methods in terms of classification, and
other pure clustering methods when there are no labels available, being also
less sensitive than previous methods to the parameters values
StarGO: A New Method to Identify the Galactic Origins of Halo Stars
We develop a new method StarGO (Stars' Galactic Origin) to identify the
galactic origins of halo stars using their kinematics. Our method is based on
self-organizing map (SOM), which is one of the most popular unsupervised
learning algorithms. StarGO combines SOM with a novel adaptive group
identification algorithm with essentially no free parameters. In order to
evaluate our model, we build a synthetic stellar halo from mergers of nine
satellites in the Milky Way. We construct the mock catalogue by extracting a
heliocentric volume of 10 kpc from our simulations and assigning expected
observational uncertainties corresponding to bright stars from Gaia DR2 and
LAMOST DR5. We compare the results from StarGO against that from a
Friends-of-Friends (FoF) based method in the space of orbital energy and
angular momentum. We show that StarGO is able to systematically identify more
satellites and achieve higher number fraction of identified stars for most of
the satellites within the extracted volume. When applied to data from Gaia DR2,
StarGO will enable us to reveal the origins of the inner stellar halo in
unprecedented detail.Comment: 11 pages, 7 figures, Accepted for publication in Ap
Adaptive Object Classification: a Mobile Robot Case Study. Internal Report 9607
An important task for any autonomous agent is to classify any objects
that it encounters in its environment. A particular type of object may be
useful to the agent, for example, or alternatively, the positions of recognizable
objects may be used as local landmarks in a cognitive map of the environment.
Biological organisms are adept at classifying new or familiar objects.
It is apparent that biological organisms often have self organizing methods
of classification and that they specialize in recognizing particular groups of
objects. Biological organisms also often incorporate information from their own
movements; rather than relying solely on sense information alone. In this
paper, these three observations inspire an adaptive scheme in which a mobile
robot learns to recognize examples from a group of complex objects. The
autonomous agent moves around each object to determine the object shape.
The important features of the shape are then extracted by a self-organizing
neural layer. Each object is then represented by a small number of feature
amplitudes making object classification more simple. The paper describes
the scheme applied to a small skid-steer mobile robot equipped with infra-red
proximity sensors
Fast training of self organizing maps for the visual exploration of molecular compounds
Visual exploration of scientific data in life science
area is a growing research field due to the large amount of
available data. The Kohonen’s Self Organizing Map (SOM) is
a widely used tool for visualization of multidimensional data.
In this paper we present a fast learning algorithm for SOMs
that uses a simulated annealing method to adapt the learning
parameters. The algorithm has been adopted in a data analysis
framework for the generation of similarity maps. Such maps
provide an effective tool for the visual exploration of large and
multi-dimensional input spaces. The approach has been applied
to data generated during the High Throughput Screening
of molecular compounds; the generated maps allow a visual
exploration of molecules with similar topological properties.
The experimental analysis on real world data from the
National Cancer Institute shows the speed up of the proposed
SOM training process in comparison to a traditional approach.
The resulting visual landscape groups molecules with similar
chemical properties in densely connected regions
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