In addition, it's multi spectral.

Identical in shape, size, spectral content.

We now talk about spectral clustering.

Taking form of a giant spectral dog.

Realtime audio processor based on frequency spectral manipulation

For instance, you see the spectral line of the gold is the only spectral line in the spectrum of the Sun.

This is called affinity based clustering or spectral clustering.

Spectral partitioning, L1 minimization. This comes from the optimization community.

They'll need a spectral micrometer to see it's a fabrication.

The fundamental idea of spectral clustering is to cluster by affinity.

So spectral clustering gets annotation of affinity to make clustering happen.

So let me look at the simple example for spectral clustering that would also work for K means or EM, but they'll be useful to illustrate spectral clustering.

And it turns out once again that spectral sensitivity is very important here.

So in summary, spectral clustering builds an affinity matrix of the data points.

One is the phong reflection model, which gives us a spectral highlight, shiny spot.

And to understand the importance of spectral clustering, let me ask you a simple intuitive quiz.

Spectral stars that are like those that make elliptical convolved with say Gaussian distribution of Doppler shifts.

You learned about K means, you learned about expectation maximization, about dimensionality reduction and even spectral clustering.

These hyper spectral images are false colored so you can it's really good for determining vegetative use.

And later we went to study the spectral stars to see is there anything strange with these objects.

The background radiation is simply too static to be able to be seen under the normal spectral analysis.

They want to actually signal their presence through these spectral lines, in the spectrum of a star, in different ways.

Let me illustrate this once again with the data set that has a different spectral clustering than a conventional clustering.

I subjected that to a special form of spectral analysis that I've been experimenting on for quite some time now.

This is the spectrum of the Sun 24,000 spectral lines, and about 15 percent of these lines is not yet identified.

Sometimes we have to deal with just one tiny, weak spectral line to measure the composition of that chemical element in the atmosphere.

Next example is analyzing the spectral emissions of stars and trying to find clusters of stars in dissimilar types that may be of interest to astronomers.

Now I understand this doesn't have a unique answer, but in the best possible case where spectral clustering works well, how many large eigenvalues do you find?

Now the key element of spectral clustering is called the affinity martrix, which is a 9 by 9 matrix in this case, where each data point gets graphed realtive to each other data point.

The first 3 items K means, EM, and dimensionality reduction are used very frequently, and spectral clustering is a rarer used method that shows some of the most recent research going on in the field.

So in this case, spectral clustering would easily assign those guys and those guys and those guys to the respective same cluster, which wouldn't be quite as easily the case for expectation maximization or k means.

So, spectral energy distribution which is the shape describes the spectrum of an object is defined to be energy per unit time per unit second per unit for frequency or wavelength and we never observe that, that's a differential unit.

And these includ I mean leading approaches from, from our book where we have bottom up detectors, which try to find boundaries based on brightness, color, and texture differences, as well as some globalization through the use of normalized cups into the spectral partitioning technique.