Archive for the ‘Self Organizing Maps (SOMs)’ Category

Self Organizing Maps

Monday, September 16th, 2013

Self Organizing Maps by Giuseppe Vettigli.

From the post:

The Self Organizing Maps (SOM), also known as Kohonen maps, are a type of Artificial Neural Networks able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. In a SOM the neurons are organized in a bidimensional lattice and each neuron is fully connected to all the source nodes in the input layer. An illustration of the SOM by Haykin (1996) is the following

If you are looking for self organizing maps using Python, this is the right place.

As with all mathematical techniques, SOMs requires the author to bridge the gap between semantics and discrete values for processing.

An iffy process at best.

Visualizing the Topical Structure of the Medical Sciences:…

Thursday, March 14th, 2013

Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach by André Skupin, Joseph R. Biberstine, Katy Börner. (Skupin A, Biberstine JR, Börner K (2013) Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach. PLoS ONE 8(3): e58779. doi:10.1371/journal.pone.0058779)

Abstract:

Background

We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.

Methodology

Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.

Conclusions

Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.

Impressive work to say the least!

But I was just as impressed by the future avenues for research:

Controlled Vocabularies

It appears that the use of indexer-chosen keywords, including in the case of a large controlled vocabulary-MeSH terms in this study-raises interesting questions. The rank transition diagram in particular helped to highlight the fact that different vocabulary items play different roles in indexers’ attempts to characterize the content of specific publications. The complex interplay of hierarchical relationships and functional roles of MeSH terms deserves further investigation, which may inform future efforts of how specific terms are handled in computational analysis. For example, models constructed from terms occurring at intermediate levels of the MeSH hierarchy might look and function quite different from the top-level model presented here.

User-centered Studies

Future user studies will include term differentiation tasks to help us understand whether/how users can differentiate senses of terms on the self-organizing map. When a term appears prominently in multiple places, that indicates multiple senses or contexts for that term. One study might involve subjects being shown two regions within which a particular label term appears and the abstracts of several papers containing that term. Subjects would then be asked to rate each abstract along a continuum between two extremes formed by the two senses/contexts. Studies like that will help us evaluate how understandable the local structure of the map is.

There are other, equally interesting future research questions but those are the two of most interest to me.

I take this research as evidence that managing semantic diversity is going to require human effort, augmented by automated means.

I first saw this in Nat Torkington’s Four short links: 13 March 2013.

Authors and Articles, Keywords, SOMs and Graphs [Oh My!]

Sunday, November 18th, 2012

Analyzing Authors and Articles Using Keyword Extraction, Self-Organizing Map and Graph Algorithms by Tommi Vatanen , Mari-sanna Paukkeri , Ilari T. Nieminen, Timo Honkela.

An attempt to enable participants at an interdisciplinary conference to find others with similar interests and to learn about other participants.

Be aware the URL given in the article for the online demo now returns a 404.

Interesting approach but be aware that if it was using Likey as described in: A Language-Independent Approach to Keyphrase Extraction and Evaluation, the absence of phrases in the reference corpus may mean the phrases are omitted from the results.

I mention that because the reference corpus was Europarl (European Parliament Proceedings Parallel Corpus).

I would not bet on the similarities between the “European Parliament Proceedings” and the “International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning.” Would you?

Leaving those quibbles to one side, interesting work, particularly if viewed as the means to explore textual data for later editing.

CiteSeer does not report a date for this paper and it does not appear in DBLP for any of the authors. Timo Honkela’s publications page gives it the following suggested BibTeX entry:

@inproceedings{sompapaper,
author = {Tommi Vatanen and Mari-Sanna Paukkeri and Ilari T. Nieminen and Timo Honkela},
booktitle = {Proceedings of the AKRR08},
pages = {105--111},
title = {Analyzing Authors and Articles Using Keyword Extraction, Self-Organizing Map and Graph Algorithms},
year = {2008},
}

Expression cartography of human tissues using self organizing maps

Saturday, November 5th, 2011

Expression cartography of human tissues using self organizing maps by Henry Wirth; Markus Löffler; Martin von Bergen; Hans Binder. (BMC Bioinformatics. 2011;12:306)

Abstract:

Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.

A nice introduction to self organizing maps (SOMs) in a bioinformatics context. Think of them as being yet another way to discover subjects about which people want to make statements and to attach data and analysis.