I'm doing a recognition problem (faces) and trying to reduce the problem size. I originally began with training data in a feature-wise coordinate system in 120 dimensions, but through PCA I found a better PC-wise coordinate system needing only 20 dimensions while still conveying 95% of the data.
I began thinking that recognition by definition is a problem of classification. Points in n-space belonging to the same object/face/whatever would cluster. To take an example, if 5 instances of the same individual are in the training data, they would cluster and the mid-point of that cluster could be numerically defined using k-means.
I have 100,000 observations, each person is represented by 5-10 headshots, this means instead of comparing a novel input to 100,000 points in my 20-space, I could instead compare to 10,000-20,000 centroids. Can k-means be used like this or have I misinterpreted? k is obviously undefined but I've been reading up on ways to find optimal k.
My specific recognition problem doesn't use neural nets but rather simple arithmetic euclidean distances between points.
