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A method and an apparatus for simultaneous highlighting of a paper document is described. The device includes a highlighter. A scanner is used and configured to capture at least one highlighted mark place on a paper document. The scanner is coupled to a memory for storing electronic versions of docu

A method and an apparatus for simultaneous highlighting of a paper document is described. The device includes a highlighter. A scanner is used and configured to capture at least one highlighted mark place on a paper document. The scanner is coupled to a memory for storing electronic versions of documents. An electronic document is accessed when a portion of the electronic document matches a portion of the paper document.

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A method and an apparatus for simultaneous highlighting of a paper document is described. The device includes a highlighter. A scanner is used and configured to capture at least one highlighted mark place on a paper document. The scanner is coupled to a memory for storing electronic versions of docu

A method and an apparatus for simultaneous highlighting of a paper document is described. The device includes a highlighter. A scanner is used and configured to capture at least one highlighted mark place on a paper document. The scanner is coupled to a memory for storing electronic versions of documents. An electronic document is accessed when a portion of the electronic document matches a portion of the paper document. ity of principal components. 4. The method of claim 1, wherein generating output data comprises generating a distribution. 5. The method of claim 1, wherein receiving the prior distribution comprises receiving one of a hierarchical and a non-hierarchical prior distribution. 6. The method of claim 1, wherein generating output data comprises determining a predictive density, including an optimal density of the data model, by marginalizing the posterior distribution over parameters of the data model. 7. The method of claim 6, wherein determining the predictive density comprises performing a Laplace approximation in conjunction with type II maximum likelihood. 8. The method of claim 6, wherein determining the predictive density comprises performing a Markov chain Monte Carlo simulation based on Gibbs sampling. 9. The method of claim 6, wherein determining the predictive density comprises performing a variational inference based on an approximation to the posterior distribution using a factorized distribution. 10. A computer-implemented method comprising: inputting a first data model of continuous variables; receiving a prior distribution of the first data model; determining a posterior distribution based at least in part upon the prior distribution; generating a second data model of optimal dimensionality based on the posterior distribution and Principal Component Analysis; and, outputting the second data model. 11. A computer-implemented method comprising: inputting a mixture of a data space having a number of components; determining a maximum number of principal components for each component of the mixture utilizing Bayesian Principal Component Analysis; and, outputting the maximum number of principal components for each component of the mixture. 12. The method of claim 11, wherein determining the maximum number of principal components comprises: receiving a prior distribution of the component; determining a posterior distribution; and, determining a predictive density, including an optimal density of the component, by marginalizing the posterior distribution over parameters of the component. 13. The method of claim 12, wherein determining the predictive density comprises performing a Laplace approximation in conjunction with type II maximum likelihood. 14. The method of claim 12, wherein determining the predictive density comprises performing a Markov chain Monte Carlo simulation based on Gibbs sampling. 15. The method of claim 12, wherein determining the predictive density comprises performing a variational inference based on an approximation to the posterior distribution using a factorized distribution. 16. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method comprising: inputting a data model; receiving a prior distribution of the data model; determining a posterior distribution based at least in part upon the prior distribution; generating output data of an optimal dimensionality based on the posterior distribution; and outputting the data model. 17. The medium of claim 16, wherein generating output data comprises generating a data model. 18. The method of claim 16, wherein generating output data comprises generating a plurality of principal components. 19. The method of claim 16, wherein generating output data comprises generating a distribution. 20. The method of claim 16, wherein receiving the prior distribution comprises receiving one of a hierarchical and a non-hierarchical prior distribution. 21. The method of claim 16, wherein generating output data comprises determining a predictive density, including an optimal density of the data model, by marginalizing the posterior distribution over parameters of the data model. 22. A computerized system comprising: a data model having a prior distribution; output data of an optimal dimensionality selected from the group essentially consisting of: a second data model, a distribution, and