Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Cross domain distribution adaptation via kernel mapping
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Evolutionary cross-domain discriminative hessian eigenmaps
IEEE Transactions on Image Processing
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Kernel discriminant analysis (KDA) is a popular technique for discriminative dimensionality reduction in data analysis. But, when a limited number of labeled data is available, it is often hard to extract the required low dimensional representation from a high dimensional feature space. Thus, one expects to improve the performance with the labeled data in other domains. In this paper, we propose a method, referred to as the domain transfer discriminant kernel learning (DTDKL), to find the optimal kernel by using the other labeled data from out-of-domain distribution to carry out discriminant dimensionality reduction. Our method learns a kernel function and discriminative projection by maximizing the Fisher discriminant distance and minimizing the mismatch between the in-domain and out-of-domain distributions simultaneously, by which we may get a better feature space for discriminative dimensionality reduction with cross-domain.