Irrelevant data variability often causes difficulties in classification and clustering tasks. For example, when data variability is dominated by environment conditions, such as global illumination, nearest-neighbour classification in the original feature space may be very unreliable. The goal of Relevant Component Analysis (RCA) is to find a transformation that amplifies relevant variability and suppresses irrelevant variability. Relevant Component Analysis tries to find a linear transformation W of the feature space such that the effect of irrelevant variability is reduced in the transformed space. That is, we wish to rescale the feature space and reduce the weights of irrelevant directions. The main premise of RCA is that we can reduce irrelevant variability by reducing the within-class variability. Intuitively, a direction which exhibits high variability among samples of the same class is unlikely to be useful for classification or clustering. … Relevant Component Analysis (RCA)