Kernel Manifold Alignment
This method addresses domain adaptation challenges for machine learning applications where labeled data is scarce across different domains, though it appears incremental as it builds on existing manifold alignment techniques.
The paper tackles the problem of aligning multiple data sources without corresponding pairs by introducing a kernel method for manifold alignment (KEMA), which shows strong performance in tasks like visual object recognition and facial expression recognition.
We introduce a kernel method for manifold alignment (KEMA) and domain adaptation that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities, performing a sort of manifold unfolding plus alignment, 3) it can define a domain-specific metric to cope with multimodal specificities, 4) it can align data spaces of different dimensionality, 5) it is robust to strong nonlinear feature deformations, and 6) it is closed-form invertible which allows transfer across-domains and data synthesis. We also present a reduced-rank version for computational efficiency and discuss the generalization performance of KEMA under Rademacher principles of stability. KEMA exhibits very good performance over competing methods in synthetic examples, visual object recognition and recognition of facial expressions tasks.