Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on inductive transfer
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 25th international conference on Machine learning
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Fast manifold learning based on riemannian normal coordinates
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Evolutionary cross-domain discriminative hessian eigenmaps
IEEE Transactions on Image Processing
Face Recognition Using Kernel UDP
Neural Processing Letters
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In recent years, transfer learning has attracted much attention in multimedia. In this paper, we propose an efficient transfer dimensionality reduction algorithm called transfer discriminative Logmaps (TDL). TDL finds a common feature so that 1) the quadratic distance between the distribution of the training set and that of the testing set is minimized and 2) specific knowledge of the training samples can be conveniently delivered to or shared with the testing samples. Drawing on this common feature in the representation space, our objective is to develop a linear subspace in which discriminative and geometric information can be exploited. TDL adopts the margin maximization to identify discriminative information between different classes, while Logmaps is used to preserve the local-global geodesic distance as well as the direction information. Experiments carried out on both synthetic and real-word image datasets show the effectiveness of TDL for cross-domain face recognition and web image annotation.