A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Mixture of the robust L1 distributions and its applications
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Twin kernel embedding with relaxed constraints on dimensionality reduction for structured data
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
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Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are Kernel PCA and Kernel Laplacian Eigenmaps introduced recently in our research. Extending our earlier work, we propose in this paper a new algorithm called Twin Kernel Embedding (TKE) that preserves the similarity structure of input data in the latent space. Experimental evaluation on MNIST handwritten digit database verifies that TKE outperforms related methods.