| 摘要 |
An information computational method for classifying multivariate datasets to identify latent (unobservable) properties of members of a sample, which properties are then used for classification. The method comprises a novel combination of statistical and fuzzy logic methods whereby the latent classes of each object are identified according to the formula (I), wherein k ELEMENT {1,...,K} indexes the directions of the multidimensional space; jk ELEMENT {1,...,Nk} identifies an object in direction k; Nk is the number of objects in principal direction k; <o>Y</o>j1,...,jk is a vector of one or more observations on a set of objects {j1,...,jK}; m ELEMENT {1,...,Mk} indexes latent classes in direction k with Mk being the number of latent classes in direction k with Mk being the number of latent classes in direction k; Skm is a latent class m in direction k; G[.] is a specified univariate or multivariate distribution; f(.) and g(.) are specified functions; and the method calculates the likelihood that each object of interest belongs to each identified latent class. The invention addresses a variety of informatics problems, particularly in the field of biology, and permits a user to make reasonable inferences about underlying cause-effect relationships, such as the underlying biology of gene-expression patterns.
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