Ph.D. Dissertation Defense “Diffusion Maps and Transfer Subspace Learning” By Olga Mendoza-Schrock

Ph.D. Committee:  Drs. Mateen Rizki, Advisor, John Gallagher, Fred Garber (EE), Michael Raymer, and Vincent Velten (AFRL)

ABSTRACT:

Transfer Subspace Learning (TSL) is beginning to gain popularity for its ability to leverage classification knowledge of one database to classify objects in a related but different database. Recently, these techniques have been informally combined with Manifold Learning and have demonstrated improved cross-dataset class recognition. One such technique combines Diffusion Maps as the Manifold Learning technique and Transfer Fisher’s Linear Discriminative Analysis (TrFLDA) as the Transfer Subspace Learning (TSL) approach. To date these approaches, while successful, have only been applied to one database containing electro-optical (EO) vehicle images. Furthermore, the technique assumed all the data, source and target domain, could be processed at the same time. In this paper, we introduce a novel extension to these techniques, referred to here as Manifold Transfer Subspace Learning (MTSL), which utilizes an out-of-sample extension (OSE) method and allows for real-time data infusion without reconstruction of the diffusion map model. This dramatically lowers computational cost of incorporating new data. As an illustration of this technique, we apply MTSL to other large, high-dimensional datasets including handwritten digits and lung and breast cancer microarray gene expressions. We achieve classification rates of 90% for cross-domain handwritten digits and rates of 87% on cross-domain breast cancer recognition. For the cancer dataset, this is significant because we are able to achieve comparable results to traditional classification methods while only utilizing one-labeled sample per class and transferring the classification from lung cancer to breast cancer.

Keywords—transfer learning; transfer subspace learning; electro-optical imaging; manifold learning; health informatics