Bipartite pattern discovery by entropy minimization-based multiple local alignment

Bipartite pattern discovery by entropy minimization-based multiple local alignment.

Many multimeric transcription factors recognize DNA sequence patterns by cooperatively binding to bipartite elements composed of half sites separated by a flexible spacer. We developed a novel bipartite algorithm, bipartite pattern discovery (Bipad), which produces a mathematical model based on information maximization or Shannon's entropy minimization principle, for discovery of bipartite sequ...

Pattern discovery via entropy minimization

We propose a framework for learning hiddenvariable models by optimizing entropies, in which entropy minimization, posterior maximization, and free energy minimization are all equivalent. Solutions for the maximum a posteriori (MAP) estimator yield powerful learning algorithms that combine all the charms of expectation-maximization and deterministic annealing. Contained as special cases are the ...

Intrinsic Images by Entropy Minimization

A method was recently devised for the recovery of an invariant image from a 3-band colour image. The invariant image, originally 1D greyscale but here derived as a 2D chromaticity, is independent of lighting, and also has shading removed: it forms an intrinsic image that may be used as a guide in recovering colour images that are independent of illumination conditions. Invariance to illuminant ...

Generalized Musical Pattern Discovery by Analogy from Local Viewpoints

Pattern Discovery for Multiple Data Sources Based on Item Rank

Retail company’s data may be geographically spread in different locations due to huge amount of data and rapid growth in transactions. But for decision making, knowledge workers need integrated data of all sites. Therefore the main challenge is to get generalized patterns or knowledge from the transactional data which is spread at various locations. Transporting data from those locations to ser...