0
$\begingroup$

I have spent sometime reading on self organizing maps. However i am still confused around a few areas.

Broadly i understand its a visual technique to reduce high dimensional data - using artificial neural networks and is unsupervised. In terms of its purpose it is pretty much similar to Tsne(Stochastic Neighbor embedding) {Please correct or add here if i am wrong or missing something}

Steps in Self Organizing broadly ( here is where i am not very confident ), please help correct/add :

  1. Initialize the weights of each node
  2. Choose a random input vector or data point from the train data
  3. Calculate best matching unit for the data point (Euclidean distance is used - distance between input vector and node is calculated)
  4. Check for neighborhood around BMU , it reduces exponentially until you reach just BMU
  5. Re-adjust weights of the BMU and the neighboring nodes so that it gets more similar to the input data point.

1.What exactly is the output of this algorithm ? i have gone through multiple sources which give multiple graphs . As a final output does it also have a graph like Tsne giving natural clusters ? or it has multiple graphs which can help in interpretation of data ? how exactly do we understand the graphs generated here ?

2.Also does it use something like elbow curve to give optimal number of clusters ?

$\endgroup$
0
$\begingroup$

Your sketch of the training procedure looks accurate.

  1. That's the idea. The output is a grid, typically 2-dimensional and rectangular, where each bin contains items that are similar to one another, and where adjacent bins are more similar than bins that are further apart. I don't know of an extension of SOMs that outputs multiple maps for disambiguation like t-SNE can produce.

  2. You can plug in whichever distance metric you want to find the BMU, and using an elbow curve seems like it may help produce a better map.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.