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- W2963689085 abstract "Abstract: In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matrices based on Renyi's axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite divisibility. The proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel Hilbert spaces. As an application example, we derive a supervised metric learning algorithm using a matrix based analogue to conditional entropy achieving results comparable with the state of the art." @default.
- W2963689085 created "2019-07-30" @default.
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- W2963689085 date "2013-01-16" @default.
- W2963689085 modified "2023-10-15" @default.
- W2963689085 title "Information Theoretic Learning with Infinitely Divisible Kernels" @default.
- W2963689085 hasPublicationYear "2013" @default.
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