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Monthly Archives: April 2013
Data Normalization
In the last post, on nearest neighbors classification, we used the “distance” between different pairs of points to decide which class each new data point should be placed into. The problem is that there are different ways to calculate distance … Continue reading
Posted in Normalization/Kernels
3 Comments
Nearest Neighbors Classification
Before we dive into nearest neighbor classification, I want to point out a subtle difference between the regression algorithm that I discussed a few posts back and what I will write about today. The goal of regression was to find … Continue reading
Posted in Classification
14 Comments
Visualization and Projection
One of the common themes that I’ve emphasized so far on this blog is that we should try to analyze high dimensional data sets without being able to actually “see” them. However, it is often useful to visualize the data … Continue reading
Posted in Visualization
3 Comments
Principal Component Analysis
Now that we’ve gotten a taste of the curse of dimensionality, lets look at another potential problem with the basic form of regression we discussedÂ a few posts back. Notice that linear/least squares regression always gives you an answer, whether or … Continue reading
Posted in Modeling
22 Comments
The curse of dimensionality
Now that we’ve had a glimpse of what it means to analyze data sets in different dimensions, we should take a little detour to consider really high dimensional data. In the discussion of regression, I suggested using your intuition about … Continue reading
Posted in Modeling
10 Comments