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- W4301152095 abstract "Stochastic kernel based dimensionality reduction approaches have become popular in the last decade. The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data. Typically, the Markov chain is fully specified by the kernel through row normalization. However, in many cases, it is desirable to impose user-specified stationary-state and dynamical constraints on the Markov chain. Unfortunately, no systematic framework exists to impose such user-defined constraints. Here, we introduce a path entropy maximization based approach to derive the transition probabilities of Markov chains using a kernel and additional user-specified constraints. We illustrate the usefulness of these Markov chains with examples." @default.
- W4301152095 created "2022-10-04" @default.
- W4301152095 creator A5036684007 @default.
- W4301152095 date "2018-06-13" @default.
- W4301152095 modified "2023-10-14" @default.
- W4301152095 title "Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reduction" @default.
- W4301152095 doi "https://doi.org/10.48550/arxiv.1806.05096" @default.
- W4301152095 hasPublicationYear "2018" @default.
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