Manifold and transfer subspace learning for cross-domain vehicle recognition in dynamic systems

Transfer Subspace Learning has gained recent popularity in the literature for its ability to perform cross-dataset and cross-domain object recognition - enablers for data fusion. The ability to leverage existing data without the need for additional data collections is attractive for Automatic Target Recognition applications. For Automatic Target Recognition (or object assessment) applications, Transfer Subspace Learning is a game changer for dynamic systems, as it enables the incorporation of sparse and dynamically collected data into existing systems that utilize large, dense databases. A baseline Transfer Subspace Learning technique is the Transfer Fisher's Linear Discriminative Analysis, an approach based on Bregman divergence-based regularization. This paper modifies the implementation of the Transfer Fisher's Linear Discriminative Analysis technique by combining it with Manifold Learning and adjusting it to allow for a more systematic search of tuning parameters. Specifically, the Diffusion Map approach is utilized, a Manifold Learning approach based on heat diffusion. The modified technique is then utilized for cross-data and cross-domain electro-optical vehicle recognition.