System for analyzing and synthesis of multi-factor data

Abstract

The present invention provides a system for analyzing multi-factor data according to a separable mixture model to classify, extrapolate, and translate data. A separable mixture model represents each data point as a function of underlying factors and corresponding parameters. With two factors, the factors can be referred to as style and content. In particular, a particular data point can be represented according to a separable mixture model, as a combination of the product of parameter vectors for each of the factors. The system accommodates symmetric and asymmetric models of interactions of parameter vectors, which are selected based upon the known data and the desired analysis. Using a selected model and known data, the system generates relevant parameters describing the known data. The parameter vector information can then be used to analyze and synthesize test data. In classification, unknown data in a new style is classified as to content, or vice versa. In extrapolation, complete content in a new style is generated from a set of observations in the new style. In translation, data having both new style and content is used to generate complete sets of content and style corresponding to the new data. The system is applicable to many types of multi-factor or 2-factor data, including typography, voice recognition, face recognition with varying poses or illuminations, and color constancy.

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