Compact Modeling of Data Using Independent Variable Group Analysis

In this paper, we introduce a modeling approach called independent variable group analysis (IVGA) which can be used for finding an efficient structural representation for a given data set. The basic idea is to determine such a grouping for the variables of the data set that mutually dependent variables are grouped together whereas mutually independent or weakly dependent variables end up in separate groups. Computation of an IVGA model requires a combinatorial algorithm for grouping of the variables and a modeling algorithm for the groups. In order to be able to compare different groupings, a cost function which reflects the quality of a grouping is also required. Such a cost function can be derived, for example, using the variational Bayesian approach, which is employed in our study. This approach is also shown to be approximately equivalent to minimizing the mutual information between the groups. The modeling task is computationally demanding. We describe an efficient heuristic grouping algorithm for the variables and derive a computationally light nonlinear mixture model for modeling of the dependencies within the groups. Finally, we carry out a set of experiments which indicate that IVGA may turn out to be beneficial in many different applications.     

Characterization of building infiltration by the tracer-dilution method

Air infiltration is an important factor in the total energy budget of a structure. It is also a significant parameter in indoor-outdoor air pollution relationships. Air infiltration cannot be reliably calculated but must be measured in a structure of interest. The tracer-dilution method is a useful technique to determine infiltration rates. This technique entails measurement of the logarithmic dilution rate of a tracer gas concentration with respect to time.     

Self organization of a massive document collection

Describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6840568 patent abstracts onto a 1002240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.     

Solidified gas samples for shock wave experimentation

A cryogenic vacuum system suitable for use with a propellant or light gas gun for performing dynamic compression experiments on solidified gases is described. An optical recording system allows the sample target (suspended in a vacuum of 10^?5 torr) to be monitored until shortly before impact. These experimental techniques have been used to measure Hugoniot data for solid argon and the first Hugoniot data for solid hydrogen.     

Text Retrieval Using Self-Organized Document Maps

A map of text documents arranged using the Self-Organizing Map (SOM) algorithm (1) is organized in a meaningful manner so that items with similar content appear at nearby locations of the 2-dimensional map display, and (2) clusters the data, resulting in an approximate model of the data distribution in the high-dimensional document space. This article describes how a document map that is automatically organized for browsing and visualization can be successfully utilized also in speeding up document retrieval. Furthermore, experiments on the well-known CISI collection [3] show significantly improved performance compared to Salton's vector space model, measured by average precision (AP) when retrieving a small, fixed number of best documents. Regarding comparison with Latent Semantic Indexing the results are inconclusive. This revised version was published online in August 2006 with corrections to the Cover Date.     

Websom for Textual Data Mining

New methods that are user-friendly and efficient are needed for guidanceamong the masses of textual information available in the Internet and theWorld Wide Web. We have developed a method and a tool called the WEBSOMwhich utilizes the self-organizing map algorithm (SOM) for organizing largecollections of text documents onto visual document maps. The approach toprocessing text is statistically oriented, computationally feasible, andscalable – over a million text documents have been ordered on a single map.In the article we consider different kinds of information needs and tasksregarding organizing, visualizing, searching, categorizing and filteringtextual data. Furthermore, we discuss and illustrate with examples howdocument maps can aid in these situations. An example is presented wherea document map is utilized as a tool for visualizing and filtering a stream ofincoming electronic mail messages.