An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
What Is Thought?
Neural Computation
Robust nonlinear dimension reduction: a self-organizing approach
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Popular nonlinear dimensionality reduction algorithms, e.g., LLE, Isomap and SIE suffer a difficulty in common: neighborhood parameter has to be configured in advance to gain meaningful embedding results. Simulation shows that embedding often loses relevance under improper parameters configures. But current embedding residual criterions of neighborhood parameters selection are not independent to neighborhood parameters. Therefore it cannot work universally. To improve the availability of nonlinear dimensionality reduction algorithms in the field of self-adaptive machine learning, it is necessary to find some transcendent criterions to achieve unsupervised parameters selection. This paper begins with a discussion of optimal embedding principles and proposes a statistics based on spatial mutual information and normalized dependency index spectrum to determine reasonable parameters configuration. The simulation supports our proposal effectively.