  Heterogeneous Domain Adaptation (HDA) addresses the transfer learning
problems where data from the source and target domains are of different
modalities (e.g., texts and images) or feature dimensions (e.g., features
extracted with different methods). It is useful for multi-modal data analysis.
Traditional domain adaptation algorithms assume that the representations of
source and target samples reside in the same feature space, hence are likely to
fail in solving the heterogeneous domain adaptation problem. Contemporary
state-of-the-art HDA approaches are usually composed of complex optimization
objectives for favourable performance and are therefore computationally
expensive and less generalizable. To address these issues, we propose a novel
Cross-Domain Structure Preserving Projection (CDSPP) algorithm for HDA. As an
extension of the classic LPP to heterogeneous domains, CDSPP aims to learn
domain-specific projections to map sample features from source and target
domains into a common subspace such that the class consistency is preserved and
data distributions are sufficiently aligned. CDSPP is simple and has
deterministic solutions by solving a generalized eigenvalue problem. It is
naturally suitable for supervised HDA but has also been extended for
semi-supervised HDA where the unlabelled target domain samples are available.
Extensive experiments have been conducted on commonly used benchmark datasets
(i.e. Office-Caltech, Multilingual Reuters Collection, NUS-WIDE-ImageNet) for
HDA as well as the Office-Home dataset firstly introduced for HDA by ourselves
due to its significantly larger number of classes than the existing ones (65 vs
10, 6 and 8). The experimental results of both supervised and semi-supervised
HDA demonstrate the superior performance of our proposed method against
contemporary state-of-the-art methods.
