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Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

机译：Fisher判别子空间中基于模型的同时聚类和可视化

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摘要

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 12 parsimonious DLM models is exhibited which allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets highlight the good performance of the proposed approach as compared to existing clustering methods while providing a useful representation of the clustered data. The method is as well applied to the clustering of mass spec-trometry data.
机译：当今，高维空间中的聚类是许多科学领域中经​​常出现的问题，但是从聚类精度和结果理解的角度来看，这仍然是一项艰巨的任务。本文提出了一个判别性潜在混合（DLM）模型，该模型将数据拟合在一个固有维数小于原始空间维数的潜在正交正交判别子空间中。通过约束组内和组之间的模型参数，展示了一个12个简约的DLM模型族，它们可以适应各种情况。还提出了一种称为Fisher-EM算法的估计算法，用于估计混合参数和可区分子空间。在模拟数据集和真实数据集上进行的实验凸显了该方法与现有聚类方法相比的良好性能，同时提供了聚类数据的有用表示。该方法也适用于质谱数据的聚类。

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