I read somewhere else that the normalized laplacian is Lnorm = D^(-1/2) L D^(-1/2) with L = D - W can you clarify why it is different here in your explanation please, thanks for you videos
i have a raman spectra for brain tumor . Is suitable to make spectral clustering for a tensor 3d(60*60*1735) where the frequencies of the spectrum are found?
@Dr. Data Science Hey I am wondering if you can help me out with a question. so let's say I have an empirical distribution of n groups and I want to cluster "distributions". Is it possible if I calculate the pairwise earthmover's distance and put it all in an adjacency matrix, and then use the clustering algorithm?
It depends on the distribution of those clusters. If you can model them using a Gaussian distribution, then use a Gaussian Mixture Model. However, I am wondering if you know the distribution of each cluster or group, why do you want to cluster data points?
@@DrDataScience I want to cluster the distributions of the groups. I have n groups, each group comes with its own distribution. But the n distributions are all roughly exponential with prob diff param. The data is tipping in different countries
for spectral clustering 1-how do you create the similarity matrix ? you mean we connect all data points with each other and assign weights based on the gaussian kernel ? 2-if so what is the variance of the gaussian distribution ? 3- i cannot imagine how to carry out the Knn on U can you elaborate more ? Thanks
1) Yes, we use the Gaussian kernel to compute similarities. 2) That's a hyperparameter that should be tuned. 3) You just need to give the matrix U as the input to k-means clustering, i.e., clusters the n rows of the matrix U.
I read somewhere else that the normalized laplacian is
Lnorm = D^(-1/2) L D^(-1/2)
with L = D - W
can you clarify why it is different here in your explanation please, thanks for you videos
It's the same thing! If you simplify it, you get the same thing.
i have a raman spectra for brain tumor . Is suitable to make spectral clustering for a tensor 3d(60*60*1735) where the frequencies of the spectrum are found?
Good idea but you need to convert the 3D tensor into 1D so you can define the similarity matrix.
@Dr. Data Science Hey I am wondering if you can help me out with a question. so let's say I have an empirical distribution of n groups and I want to cluster "distributions". Is it possible if I calculate the pairwise earthmover's distance and put it all in an adjacency matrix, and then use the clustering algorithm?
It depends on the distribution of those clusters. If you can model them using a Gaussian distribution, then use a Gaussian Mixture Model. However, I am wondering if you know the distribution of each cluster or group, why do you want to cluster data points?
@@DrDataScience I want to cluster the distributions of the groups. I have n groups, each group comes with its own distribution. But the n distributions are all roughly exponential with prob diff param. The data is tipping in different countries
Hello sir... I didn't get the output.. No error also
How do we check convergence in knn ?
Good question! You can plot the value of the cost function vs the number of iterations.
How do we access the performance of knn ? What performance metrics should be used ?
Great question as well! I will post another video on how to evaluate any clustering method. A popular one is normalized mutual information or NMI.
for spectral clustering
1-how do you create the similarity matrix ? you mean we connect all data points with each other and assign weights based on the gaussian kernel ?
2-if so what is the variance of the gaussian distribution ?
3- i cannot imagine how to carry out the Knn on U can you elaborate more ?
Thanks
1) Yes, we use the Gaussian kernel to compute similarities.
2) That's a hyperparameter that should be tuned.
3) You just need to give the matrix U as the input to k-means clustering, i.e., clusters the n rows of the matrix U.
@@DrDataScience so for U of dim 4x3 we need to cluster the 12 points we have in U into 3 clusters ?
Let's say you want to find k=2 clusters and U is 4x3. Then, you want to cluster 4 data points each represented by 3 features into 2 groups.