Monthly Archives: February 2018

Data Science Done Right (the Kitchen Style) #17

nD 2-means disjunctive pointwise clustering When trying to come up with association of our 1D “disjoint delta” measure to entities of n-dimensional space, we would notice that for 2-means case it naturally associates to the dot/inner product of delta vectors xi … Continue reading

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Data Science Done Right (the Kitchen Style) #16

Clustering So far, when we were talking about linear methods, we were using terms linear/vector space and dataset in that space quite interchangeable, which is not very rigorous, but, excusable if it is obvious from the context. Anyway, we were … Continue reading

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Data Science Done Right (the Kitchen Style) #15

So far we’ve been working with the ECG dataset as a “horizontal” matrix, with ECG channels, being indeed information channels, or dimensions of our vector space, and readings at time slices – our data n-dimensional (actually 8-dimensional) points of count … Continue reading

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Data Science Done Right (the Kitchen Style) #14

The ideas in PCA of “dropping” some dimensions of the resulting eigenbasis (after basis rotation with the target in mind of getting the covariance matrix in the lamda form), or decomposing our vector space into subspaces (“linear modelling” and “error”, … Continue reading

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