Talk:Self-organizing map

message: Looks interesting, but this is not a place for academics to promote concepts not widely discussed - somewhere other than their own pet web site.  Please outline what an SOM actually is, with examples, so that this can be a proper article. —The preceding unsigned comment was added by  142.177.93.97 (talk) 22:57, 24 January 2003
:Agreed, it still needs some explaination. But please, the entry was already there (I did not start a new item), and I think I added quite some good links (not my own 'pet-web' by the way). Are you anti-academic? Pieter Suurmond 00:25 Jan 25, 2003 (UTC)
::Yes, I am anti-academic.  So what?  The rules are the rules, and the idea must be explained here fully.  So, for instance, you must explain exactly what you mean by wikipedia being "an example of" this paradigm.  If you mean it is an example of self-organizing systems, then that belongs in that article, not in this article about something more specific. —The preceding unsigned comment was added by  142.177.82.218 (talk) 02:53, 25 January 2003
:::Kohonen Maps are far from being a 'concept not widely disscussed' : international conferences about data analysis and neural networks almost always have special sessions dedicated to it.  It also has its own bi-annual international conference : WSOM held in Japan two years ago and in France this year. Nevertheless, the article needs a lot of work to present the SOM adequately. [df] —The preceding unsigned comment was added by  130.104.239.182 (talk) 10:03, 8 August 2005
:"...and several thousand scientific articles have been written about it." - The fact that an anti-academic on wikipedia is unfamiliar with Kohonen Maps and calls them something "not widely discussed" probably doesn't justify this clause. It adds no more useful information than mentioning "several thousand journal articles have been written about the concept" would add to the Riemannian manifold page, for example. A mention of a few key articles or applications would be more informative. Geo.per 19:01, 26 June 2006 (UTC)
thread_title:  Old comments 

message: I deleted following sentence: "Unsupervised learning" is, technically, supervised in that we do have a desired output.
It doesn't make sense, what is the "desired output"?
Update: Now I think I understand that this probably refers to the fact that weights are moved towards the input vector. I still don't think this justifies calling SOM supervised, because there is no a priori desired output value for an input. AnAj 15:39, 18 June 2006 (UTC)
thread_title:  desired outputs? 

message: The first line reads "The self-organizing map (SOM) is a subtype of artificial neural networks", that's like me saying "a toyota is a subtype of automobile".  Can we clear this up please Paskari 21:38, 1 December 2006 (UTC)
thread_title: Subset Issue

message: I removed "gnod, a Kohonen network application." because I don't see any evidence saying that it is and on the gnod page, there are references to sites that say it's not. I added WEBSOM, because it is. --JamesBrownJr 22:47, 2 March 2007 (UTC)
thread_title:  Examples 

message: The following appears to contradict what is said in Generative Topographic Map about topology preservation."It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space."
I would say that it cannot preserve the topological properties of the input space for all possible inputs. Perhaps you should change the sentence to: "It is trained using unsupervised learning to produce low dimensional representation of the training samples while trying to preserve the topological properties of the input space."
     The opinion that this contradicts the GTM article is not accurate.  Both algorithms can be expected to produce
     topological error; it is therefore a matter of degree.  Typically one would run the SOM algorithm several times
     and measure the topological error.  Depending on how important this issue is for the analyst, he or she would
     select the map with the lowest topological error.  I am removing the contradiction warning but I will add more
     information on SOM vs GTM.  —Preceding unsigned comment added by ElectricTypist (talk • contribs) 15:48, 21 November 2007 (UTC)
thread_title: Contradiction with Generative Topological Map article

message: I here by state this article as 'awesome', its a good reference for anyone who wants specific information reguarding the workings of this type of neural network, instead of a smear of general information and no useful engineering qualities at all. --Chase-san 11:28, 4 August 2007 (UTC)
replies: text: It's a good article, certainly. I don't understand "toroidal grids", "U-matrices" or such but this vector stuff made me understand the general principles. I think I might be able program some kind of SOM based on the article. ... said: Rursus (bork²) 20:29, 2 May 2009 (UTC)
thread_title:  Awesome reference 

message: Without any kind of description and context, those pictures are completely meaningless.  What do the colours mean? What is the form of training data? What are the inputs?  How do you generate the image from the final sets of weight vectors?  How do you interpret the pictures.  Deeply non-awesome. -- GWO  —Preceding unsigned comment added by Gareth Owen (talk • contribs) 12:44, 12 September 2007 (UTC)
replies: text: True. I have added a description of what is in the picutre. There's not enough space to describe all that you ask for - a larger article section would be required for that. It is however a standard visualization style for SOMs. In the external links section there are a bunch of references that go deeper into SOM visualization. --Denoir 18:55, 20 September 2007 (UTC)
replies: text: I would like to see some explanation of what you can learn from this visualiztion.  Otherwise, it just looks like a pretty picture with a link to a product. --Skremer (talk) 11:55, 7 January 2008 (UTC)
thread_title:  Picture 

message: ITEM 1.
The first paragraph of the article contains the statement that "The map seeks to preserve the topological properties of the input space."  This is a reasonable aim, but one that is virtually impossible with the SOM on all but the most trivial of data.  It also gives the impression that the SOM generates a topology preserving map.
To improve the article, the sentence "The map seeks to preserve the topological properties of the input space." should be replaced with "The SOM map aims to exploit the topographic relationships of the network nodes to provide more information about the underlying data space than is possible with the regular Vector Quantisation approach."
ITEM 2.
"Interpretation
There are two ways to interpret a SOM. Because in the training phase weights of the whole neighborhood are moved in the same direction, similar items tend to excite adjacent neurons. Therefore, SOM forms a semantic map where similar samples are mapped close together and dissimilar apart."
This last sentence is misleading and wrong.  This is only true if there is no violation in the topological mapping of the SOM map.  Graph dimensionality, graph size and graph folding can cause violations in the SOM mapping.    
Let me expand on this:
Neural maps aim to exploit the topographic relationships of the network nodes to provide more information about the underlying data space than is possible with the regular Vector Quantisation approach. The neural map aims to 1. link the relationships between input data with the relationships between the nodes of the graph, and 2. for the map to be truly useful, the relationships between nodes of the graph should be reflected in the relationships within the input data.  This is very important, and is where the SOM approach simply fails on all but the the most trivial of data.  In topology preserving mappings, neighbourhoods are preserved in the transformations from data to graph space and vice-versa.
I suggest anybody interested in why the SOM can not be considered a topological mapping reads [H. U. Bauer, M. Herrmann, and T. Villmann. Neural maps and topographic vector quantization. Neural Networks, 12:659-676, 1999].   The SOM (at best) is a topographic mapping - it is not a topological mapping.  The Growing Neural Gas algorithm [B. Fritzke. A Growing Neural Gas Network Learns Topologies. In G. Tesauro, D.S. Touretzky, and T.K. Leen, editors, Advances in Neural Processing Systems 7 (NIPS'94), pages 625-632, Cambridge, 1995. MIT Press.] (tends to) generate a topology preserving mapping.  This is a well known fact, and this article is misleading with its incorrect use of terminology.     
To improve this article, "Therefore, SOM forms a semantic map where similar samples are mapped close together and dissimilar apart."  needs much more work.  This statement is only true if the map contains no topographic errors, which is difficult to achieve on all but the most trivial of data.
FORTRANslinger (talk) 20:05, 19 December 2007 (UTC)
thread_title:  Topography vs. Topology 

message: Can somebody explore on the history of the subject?
It must be at least, starting 1982.
However, there must be a chronological list of related subjects developed before SOM.
(k-means, hard c-means, Linde-Buzo-Gray algorithm and other clustering stuff?)  —Preceding unsigned comment added by Arkadi kagan (talk • contribs) 21:33, 19 January 2010 (UTC)
thread_title:  History section 

message: One of the references is password-protected. This does not seem appropriate for WP:
# ^ Ultsch A (2003). U*-Matrix: a tool to visualize clusters in high dimentional data. University of Marburg, Department of Computer Science, Technical Report Nr. 36:1-12.
Rmkeller (talk) 06:17, 2 November 2010 (UTC)
thread_title:  Improper Reference 

message: I was interested in the reference supporting this sentence:
"It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character. [5]"
However it points to the website http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Self-organizing_map.html, which is a mere copy of this wikipedia article.... how could that happen?  — Preceding unsigned comment added by 141.14.31.91 (talk) 10:48, 28 January 2015 (UTC)
thread_title:  Another Improper Reference 

message: Why is the self-organizing map considered to be a type of artificial neural network? What is it that is "neural" about them? —Kri (talk) 12:55, 2 January 2017 (UTC)
thread_title:  Neural network?