Topic: cPCA*

Living organisms receive an immense amount of information per unit time, but much of it is of little relevance for behavior. A simple approach to process this high-dimensional input is to focus on a lower-dimensional subspace and ignore the directions that are less informative. Principal component analysis (PCA) achieves this by discarding the directions with low variance. Such an approach is, however, inefficient in cases where the irrelevant directions are very noisy, thus having greater variance than the relevant ones. If we have access to representative samples of variability in irrelevant