Abstract

The idea of using tensor decompositions as a parametric model for group data analysis is developed. Two models (deterministic and probabilistic) based on block term decomposition are presented using various formats of terms. The relationship between block term decomposition and mixtures of continuous latent probabilistic models is established; specifically, a mixture distribution model with a structured representation is constructed relying on block term decomposition. The models are tested as applied to the problem of clustering a set of color images and brain electrical activity data. The results show that the proposed approaches are capable of extracting a relevant individual component of the data.

中文翻译：

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使用块项分解构造数据：将联合张量分解和变分块项分解作为参数化混合分布模型

摘要

开发了使用张量分解作为组数据分析的参数模型的想法。使用各种形式的术语呈现了基于块术语分解的两种模型（确定性和概率性）。建立了块项分解与连续潜在概率模型混合的关系；具体而言，基于块项分解构建了具有结构化表示的混合分布模型。这些模型经过测试，适用于对一组彩色图像和大脑电活动数据进行聚类的问题。结果表明，所提出的方法能够提取数据的相关单个组件。