NMR spectral analysis using prior knowledge

Supervised HDP Using Prior Knowledge

End users can find topic model results difficult to interpret and evaluate. To address user needs, we present a semi-supervised hierarchical Dirichlet process for topic modeling that incorporates user-defined prior knowledge. Applied to a large electronic dataset, the generated topics are more fine-grained, more distinct, and align better with users’ assignments of topics to documents.

Linear prediction spectral analysis of NMR data

Versatile fitting tool for simultaneous modeling of spectral arrays using prior knowledge restrictions in two dimensions

Fig 3. Experimental 2DJ data from a lactate phantom with 2D fit and residue. PRESS with TE from 21ms to 331ms in steps of 10ms, recorded at 3T. Fig 1. Fitted time series of spectra obtained after an oral histidine load at time 0. The red signals with varying amplitude originate from histidine, the green doublet from the amide proton of NAA, and the blue lines from the rest of the downfield spec...

Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images

We propose a Bayesian smoothness prior in the spectral fitting of magnetic resonance spectroscopic images which can be used in addition to commonly employed prior knowledge. By combining a frequency-domain model for the free induction decay with a Gaussian Markov random field prior, a new optimization objective is derived that encourages smooth parameter maps. Using a particular parametrization...

Interesting Patterns Extraction Using Prior Knowledge

One important challenge in data mining is to extract interesting knowledge and useful information for expert users. Since data mining algorithms extracts a huge quantity of patterns it is therefore necessary to filter out those patterns using various measures. This paper presents IMAK, a part-way interestingness measure between objective and subjective measure, which evaluates patterns consider...