Titlebook: Knowledge Discovery for Business Information Systems; Witold Abramowicz,Jozef Zurada Book 2002 Springer Science+Business Media New York 20

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Sau Dan Lee,David Cheungta vectors is very large. The spectral decomposition of a large dimensioanl kernel encounters difficulties in at least three aspects: large memory usage, high computational complexity, and computational instability. Although the kernels in some nonlinear DR methods are sparse matrices, which enable

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D. R. Mani,James Drew,Andrew Betz,Piew Dattaetween the pairs of all neighbors of each point in the data set. Since the method keeps the local maximum variance in dimensionality reduction processing, it is called maximum variance unfolding (MVU). Like multidimensional scaling (MDS), MVU can be applied to the cases that only the local similarit

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Jan Mrazekpect to any statistical divergence like the Kullback–Leibler (KL) divergence or the Hellinger divergence. When equipping a statistical manifold with the KL divergence, the induced manifold structure is dually flat, and the KL divergence between distributions amounts to an equivalent Bregman divergen

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Jaroslav Pokornyel considers the statistical mechanics of dynamic systems in their “space of evolution” associated to a homogeneous symplectic manifold by a Lagrange 2-form, and defines in case of non-null cohomology (non equivariance of the coadjoint action on the moment map with appearance of an additional cocyle

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Giuseppe Psailaeir properties. We then define the strictly quasiconvex Bregman divergences as the limit case of scaled and skewed quasiconvex Jensen divergences, and report a simple closed-form formula which shows that these divergences are only pseudo-divergences at countably many inflection points of the quasico

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Srinivasan Parthasarathy,Mohammed J. Zaki,Mitsunori Ogihara,Sandhya Dwarkadass encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statisti

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