I've been trying to teach myself some machine learning, and I wanted to ask what seems a simple question, but I've not been able to find any resources that explain the next step.
Let's say I am doing semi-supervised learning, and have a few hundred sparsely distributed feature extractors, trained on random 32x32 regions (the unsupervised part of the process).
I now want to take the larger images in my training set, and do some supervised learning based on the feature extractors I now have. In this case, multi-label classification.
The bit I'm not clear on is what I do with the full sized image from my training set:
- Take random samples from it? -- seems like it would be pot luck if it picks an area needed to identify appropriate labels
- Take overlapping tiles with a sliding window? -- seems like I'd end up with absurd dimensionality, since for each tile, I get a whole vector of features
- Take adjacent tiles? -- dimensionality still nonsensical, and probably translation sensitive as well
It's a hypothetical example, but let's say my inputs are 800x600 photographs, i.e. the input is about 100-1000 times bigger than the samples I used in the unsupervised learning stage.