The nature of statistical learning theory
The nature of statistical learning theory
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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In many data-mining applications, Support Vector Machines are used to learn query concepts, and then the learned SVM is used to find the corresponding best matches in a given dataset. When the dataset is large, naively scanning the entire dataset to find the instances with the highest classification scores is not practical. An indexing strategy is thus desirable for scalability. In contrast to queries in traditional similarity search scenarios which are in the form of an input space point, SVM queries are hyperplanes in a (kernel function induced) feature space, and the best matches are instances farthest from the hyperplane. Also, the kernel parameters used, and hence the feature space used, may vary with the query. These issues make the problem challenging. In this work, we propose an indexing strategy that uses pivots (selected using PCA or KPCA) to prune irrelevant instances from the dataset, and zoom in on a smaller candidate set, to efficiently answer SVM queries.