LEARNING OUT-OF SAMPLE MAPPING IN NON-VECTORIAL DATA REDUCTION USING CONSTRAINED TWIN KERNEL EMBEDDING
Twin Kernel Embedding (TKE) Is a powerful non vectorial data redaction algorithm proposed recently for advanced applications In clustering and visualization, manifold learning, etc Due to the requirement of online processing in many cutting edge research problems Involving highly structured data like DNA, protein sequences and biometric features that are non-vectorial In nature, learning the out-of-sample (OOS) mapping becomes a necessity.To address this, we propose Constrained TKE, which is an OOS extension of TKE capable of learning such a mapping function.This is achieved by including the mapping in the objective function optimized by the TKE algorithm.More broadly, this mapping function can be applied In other data reduction methods as an OOS extension.Furthermore, to improve the accuracy of predictions in case where new samples are presented in batch, a refinement strategy is Introduced by exploiting the similarity between new samples which is often ignored by other methods.Experimental results on the Reuters-21578 text collection confirmed the usefulness of the proposed method.
Dimensionality reduction TKE Out-Of-Sample
Yi Guo Junbin Gao Paul W.Kwan
School of Math, Stat.& Computer Science, University of New England, Armidale, NSW 2351, Australia School of Computer Science, Charles Sturt University, Bathurst, NSW 2795, Australia
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
19-24
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）