5,809 research outputs found
Analysis of Professional Trajectories using Disconnected Self-Organizing Maps
In this paper we address an important economic question. Is there, as
mainstream economic theory asserts it, an homogeneous labor market with
mechanisms which govern supply and demand for work, producing an equilibrium
with its remarkable properties? Using the Panel Study of Income Dynamics (PSID)
collected on the period 1984-2003, we study the situations of American workers
with respect to employment. The data include all heads of household (men or
women) as well as the partners who are on the labor market, working or not.
They are extracted from the complete survey and we compute a few relevant
features which characterize the worker's situations. To perform this analysis,
we suggest using a Self-Organizing Map (SOM, Kohonen algorithm) with specific
structure based on planar graphs, with disconnected components (called D-SOM),
especially interesting for clustering. We compare the results to those obtained
with a classical SOM grid and a star-shaped map (called SOS). Each component of
D-SOM takes the form of a string and corresponds to an organized cluster. From
this clustering, we study the trajectories of the individuals among the classes
by using the transition probability matrices for each period and the
corresponding stationary distributions. As a matter of fact, we find clear
evidence of heterogeneous parts, each one with high homo-geneity, representing
situations well identified in terms of activity and wage levels and in degree
of stability in the workplace. These results and their interpretation in
economic terms contribute to the debate about flexibility which is commonly
seen as a way to obtain a better level of equilibrium on the labor market
An empirical study of neighbourhood decay in Kohonen\u27s self organizing map
In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids the need for sophisticated learning gain decay schemes, and precludes the need for a priori knowledge of likely training times. This scheme also has some potential uses for continuous learning
Optimising the topology of complex neural networks
In this paper, we study instances of complex neural networks, i.e. neural
netwo rks with complex topologies. We use Self-Organizing Map neural networks
whose n eighbourhood relationships are defined by a complex network, to
classify handwr itten digits. We show that topology has a small impact on
performance and robus tness to neuron failures, at least at long learning
times. Performance may howe ver be increased (by almost 10%) by artificial
evolution of the network topo logy. In our experimental conditions, the evolved
networks are more random than their parents, but display a more heterogeneous
degree distribution
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
Real-Time Analysis of Correlations Between On-Body Sensor Nodes
The topology of a body sensor network has, until recently, often been overlooked; either because the layout of the network is deemed to be sufficiently static (”we always know well enough where sensors are”), we always know exactly where the nodes are or because the location of the sensor is not inherently required (”as long as the node stays where it is, we do not need its location, just its data”). We argue in this paper that, especially as the sensor nodes become more numerous and densely interconnected, an analysis on the correlations between the data streams can be valuable for a variety of purposes. Two systems illustrate how a mapping of the network’s sensor data to a topology of the sensor nodes’ correlations can be applied to reveal more about the physical structure of body sensor networks
eXamine: a Cytoscape app for exploring annotated modules in networks
Background. Biological networks have growing importance for the
interpretation of high-throughput "omics" data. Statistical and combinatorial
methods allow to obtain mechanistic insights through the extraction of smaller
subnetwork modules. Further enrichment analyses provide set-based annotations
of these modules.
Results. We present eXamine, a set-oriented visual analysis approach for
annotated modules that displays set membership as contours on top of a
node-link layout. Our approach extends upon Self Organizing Maps to
simultaneously lay out nodes, links, and set contours.
Conclusions. We implemented eXamine as a freely available Cytoscape app.
Using eXamine we study a module that is activated by the virally-encoded
G-protein coupled receptor US28 and formulate a novel hypothesis about its
functioning
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