Development of Fast Incremental Slow Feature Analysis (F-IncSFA)

The proposed Fast Incremental Slow Feature Analysis (F-IncSFA) which is considered as unsupervised learning and it can be used for extracting the features. The featurescan represent the fundamental components of the modifications in different aspect and especially in posing and temporally firms and consistent even in high-dimensional input like signal, video, etc. Here, we addressed a development in SFA algorithm as compare with latest one [17] by combining Candid Covariance-Free Incremental Principle components Analysis (CCIPCA) and Minor Components Analysis (MCA).The proposed F-IncSFA can adapts along with non-stationary environments and unlike the latest SFA, which has two times using CCIPCA, has one time using CCIPCA in its algorithm which makes the method simpler yet efficient. We examine the proposed approach by using some video sequences of humanoid robot and also it is compared with CCIPCA in several experiments and the result indicates that it indeed has superior outcome and impart informative slow features that is representing significant abstract from possessions of non-stationary environment and poses. We successfully apply the F-IncSFA on the high-dimensional video and extract abstract object data. We extend our F-IncSFA to networks in hierarchical model, and apply it for extraction of features in the information obtained from high-dimensional video and the results were promising.