Self organizing Maps and Linear vector quantization

Question

Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM's are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM's in LVQ, LVQ should be provided with a labelled training data set. But since SOM's only do clustering and not classification and thus cannot have labelled data how can SOM be used as an input for LVQ?

Does LVQ fine tune the clusters in SOM?
Before using in LVQ should SOM be put through another classification algorithm so that it can classify the inputs so that these labelled inputs maybe used in LVQ?

Answer 1

It must be clear that supervised differs from unsupervised because in the first the target values are known. Therefore, the output of supervised models is a prediction. Instead, the output of unsupervised models is a label for which we don't know the meaning yet. For this purpose, after clustering, it is necessary to do the profiling of each one of those new label.

Having said so, you could label the dataset using an unsupervised learning technique such as SOM. Then, you should profile each class in order to be sure to understand the meaning of each class. At this point, you can pursue two different path depending on what is your final objective: 1. use this new variable as a way for dimensionality reduction 2. use this new dataset featured with the additional variable representing the class as a labelled data that you will try to predict using the LVQ

Hope this can be useful!