The clustering models we covered in the previous chapter can also be used for a form of dimensionality reduction. This works in the following way:
- Assume that we cluster our high-dimensional feature vectors using a K-means clustering model, with k clusters. The result is a set of k cluster centers.
- We can represent each of our original data points in terms of how far it is from each of these cluster centers. That is, we can compute the distance of a data point to each cluster center. The result is a set of k distances for each data point.
- These k distances can form a new vector of dimension k. We can now represent our original data as a new vector of lower dimension relative to the original feature ...