Hierarchical Incremental Slow Feature Analysis

Slow feature analysis (1) (SFA) is an unsupervised learning technique that extracts features from an input stream with the objective of maintaining an informa- tive but slowly-changing feature response over time. Due to some promising results so far (1,2), SFA has an intriguing potential for autonomous agents that learn upon raw visual streams, but in order to realize this potential it needs to be both hierarchical and adaptive. An incremental version of Slow Feature Analysis, called IncSFA, was recently introduced (2,3,4). Here, we focus on its hierarchical extension (H-IncSFA). H-IncSFA networks are composed of multiple layers of overlapping IncSFA units, where each unit has a local receptive eld. Figure 1 shows an example H-IncSFA network, based on the one specied by Franzius et al. (5).