Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 178-183
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From page 178... ...
r wile explain this a little bit. The Idea is that these algorithms train on the training (lata, and then they end up with an algorithm that is a trained algorithm that you can hand data to, and it will hand you back a distance matrix.
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From page 179... ...
Now, the pre-processing is done to all the training data and all the test data; you shift, scale and rotate based on eye coordinates. So, these slides aren't actually the very latest.
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From page 180... ...
So, what we are going to now is, we are going to try to modify this covariance matrix, which uses a kernel to build the distance, or the inverse of that kernel, modify it with the knowledge that differences along this direction don't matter. Now, of course, that is not quite true, because if this is highly curved, and if ~ go too far out, that linear approximation isn't very good.
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From page 181... ...
It gets a little too light here and dark there. So, there are some artifacts with this secondorder of correction, because we have shifted it a little bit past the validity of this combination of kernels and image.
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From page 182... ...
Do you want to make a comment on the data fusion, data assimilation question earlier?
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From page 183... ...
You can correct me if you know better. Data fusion could include something like that.
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