Curiosity driven incremental LDA agent active learning

This paper presented a novel active linear discriminant analysis (LDA) learning method in the form of curiosity-driven incremental LDA (cILDA) and multiple cILDA agents cooperative learning (mcILDA). The curiosity in psychology here is modelled mathematically as a discriminability residue in-between instance space and its corresponding eigenspace. As the learning proceeds, the curiosity of an individual agent updates over time by two incremental learning processes: One updates the characterization of eigenspace and another re-calculates the curiosity. In the multi-agent scenario, individual agent communicates and cooperates with each other at every learning stage to discover the discriminant characterization of the whole pattern. In the experiment, we described how the discriminative instances could be significantly selected based on the curiosity with, at most, minor sacrifices in learning rate and classification accuracy. The experimental results show that the proposed curiosity learning performs gracefully under different level of redundancy, and the proposed cILDA/mcILDA learning system is capable of learning less instances, but has more often an improved discrimination performance.