Manifold Warping: Manifold Alignment over Time

Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., documents written in different languages). Recent work on manifold learning has shown that data collected in real-world settings often have high-dimensional representations, but lie on low-dimensional manifolds. Furthermore, data sets collected from similar generating processes often present different high-dimensional views, even though their underlying manifolds are similar. The ability to align these data sets and extract this common structure is critical for many transfer learning tasks. In this paper, we present a novel framework for aligning two sequentially-ordered data sets, taking advantage of a shared low-dimensional manifold representation. Our approach combines traditional manifold alignment and dynamic time warping algorithms using alternating projections. We also show that the previously-proposed c...

• Type: Journal
• Year: 2012
• Where: AAAI
• Authors: Hoa Trong Vu, Clifton Carey, Sridhar Mahadevan
• Keywords: AAAI 2012, Alignment Methods, Dimensional Representations, Intelligent Agents, Target Domains