CODEWITHSUNDEEP
machine-learningcluster-analysisspectral
Potemkin
Potemkin

Reputation: 111

How to cluster a given sample to class centers calculated by spectral clustering algorithm?

Suppose we got several centers {C1(d1, d2...dn), C2...} with training samples according to spectral clustering algorithm. If a new test sample vector (x1, ... xn) is given, what should I do to get it into a class?

Note that, the similarity matrix we used in spectral clustering process is not only based on Euclidean distance between training vectors but geodesic distance. So the distance can not be calculated with just two vectors, and the class center is not so easy to get as what we can in K-means.

One solution I have got is k-nearest neighbour algorithm. Are there any other solutions?

Upvotes: 2

Views: 591

Answers (1)

Amro
Amro

Reputation: 124563

In the case of spectral clustering, the result is not updatable in that if you add another instance/vector, you will have to repeat the whole process by recomputing the affinity/laplacian matrix, performing eigen-decomposition, then clustering the rows of the reduced matrix.

Upvotes: 2

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