A Vector Quantization Approach for Life-Long Learning of Categories

We present a category learning vector quantization (cLVQ) approach for incremental and life-long learning of multiple visual categories where we focus on approaching the stability-plasticity dilemma. To achieve the life-long learning ability an incremental learning vector quantization approach is combined with a category-specific feature selection method in a novel way to allow several metrical "views" on the representation space for the same cLVQ nodes.

[1]  Robert B. Fisher,et al.  Incremental One-Class Learning with Bounded Computational Complexity , 2007, ICANN.

[2]  R. French.  Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.

[3]  Shigeo Abe,et al.  Incremental learning of feature space and classifier for face recognition , 2005, Neural Networks.

[4]  Martin A. Riedmiller,et al.  Incremental GRLVQ: Learning relevant features for 3D object recognition , 2008, Neurocomputing.

[5]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[6]  Fred Henrik Hamker,et al.  Life-long learning Cell Structures--continuously learning without catastrophic interference , 2001, Neural Networks.

[7]  Heiko Wersing,et al.  A Comparison of Features in Parts-Based Object Recognition Hierarchies , 2007, ICANN.

[8]  R. French,et al.  Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and Solutions , 1994 .

[9]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[10]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[11]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[12]  Thomas Villmann,et al.  Generalized relevance learning vector quantization , 2002, Neural Networks.

[13]  Heiko Wersing,et al.  A biologically motivated visual memory architecture for online learning of objects , 2008, Neural Networks.