Abstract
A kernel-matrix based distance measure is utilized for computing the similarities between the data points. The available few labeled data is used as constraints to project on initial kernel-matrix using Bregman projection. Since the projection of constraints onto the matrix is not orthogonal, we need to identify an appropriate subset of constraints subject to objective functions, measuring the quality of partitioning of the data. As the kernel-space is large in size, we have divided the original kernel space into multiple kernel sub-spaces so that each kernel can be processed independently and parallelly in advance GPU and kernel semi-supervised metric learning using multi-objective approach is applied on individual kernels parallelly. The multi-objective framework is used to select the best subset of constraints to optimize multiple objective functions for grouping the available data. Our approach outperforms the state of the art algorithms on the various datasets with respect to different validity indices.
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Sanodiya, R.K., Saha, S., Mathew, J. (2018). A Multi-kernel Semi-supervised Metric Learning Using Multi-objective Optimization Approach. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_47
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DOI: https://doi.org/10.1007/978-3-030-04179-3_47
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