On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Towards systematic design of distance functions for data mining applications
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive tree similarity learning for image retrieval
Multimedia Systems
Formulating context-dependent similarity functions
Proceedings of the 13th annual ACM international conference on Multimedia
Estimating Sales Opportunity Using Similarity-Based Methods
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Kernel Alignment k-NN for Human Cancer Classification Using the Gene Expression Profiles
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Learning a combination of heterogeneous dissimilarities from incomplete knowledge
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Semi-supervised clustering using heterogeneous dissimilarities
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Fusing heterogeneous data sources considering a set of equivalence constraints
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
An affinity-based new local distance function and similarity measure for kNN algorithm
Pattern Recognition Letters
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Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a context-dependent (also application-, data-, and user-dependent) way. In this paper, we propose to learn a distance function by capturing the nonlinear relationships among contextual information provided by the application, data, or user. We show that through a process called the "kernel trick," such nonlinear relationships can be learned efficiently in a projected space. Theoretically, we substantiate that our method is both sound and optimal. Empirically, using several datasets and applications, we demonstrate that our method is effective and useful.