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首页> 外文学位 >Non-redundant clustering, principal feature selection and learning methods applied to lung tumor image-guided radiotherapy.
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Non-redundant clustering, principal feature selection and learning methods applied to lung tumor image-guided radiotherapy.

机译：非冗余聚类，主要特征选择和学习方法应用于肺肿瘤图像引导放疗。

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This thesis is divided into two parts. The first part is about non-redundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor image-guided radiotherapy.;In the first part, we investigate a new clustering paradigm for exploratory data analysis: find all non-redundant clustering views of the data, where data points of one cluster can belong to different clusters in other views. Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. We present a framework to solve this problem and suggest two approaches: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces.;The idea of removing redundancy between clustering solutions was inspired by our preliminary work on solving the feature selection problem via transformation methods. In particular, we developed a feature selection method based on the popular transformation approach: principal component analysis (PCA). PCA is a dimensionality reduction algorithm that do not explicitly indicate which variables are important. We designed a method that utilize the PCA result to select the original features, which are most correlated to the principal components and are as uncorrelated with each other as possible through orthogonalization. We show that our feature selection method, as a consequence of orthogonalization, preserve the special property in PCA that the retained variance can be expressed as the sum of orthogonal feature variances that are kept.
机译：本文分为两个部分。第一部分是关于高维数据的非冗余聚类和特征选择。第二部分是将学习技术应用于肺肿瘤图像引导的放射治疗。在第一部分中，我们研究用于探索性数据分析的新聚类范例：找到数据的所有非冗余聚类视图，其中一个聚类的数据点在其他视图中可以属于不同的群集。典型的聚类算法输出数据的单个聚类。但是，在实际应用中，数据可以具有不同的分组，这些分组从不同的角度来看都是合理且有趣的。对于高维数据，尤其是这样，其中不同的特征子空间可能揭示数据的不同结构。我们提出了一个解决这个问题的框架，并提出了两种方法：（1）正交聚类和（2）正交子空间中的聚类。;消除聚类解决方案之间的冗余的想法受到了我们通过解决特征选择问题的初步工作的启发转换方法。特别是，我们基于流行的转换方法开发了一种特征选择方法：主成分分析（PCA）。 PCA是降维算法，没有明确指出哪些变量很重要。我们设计了一种方法，该方法利用PCA结果选择原始特征，这些特征与主成分最相关，并且通过正交化彼此之间尽可能不相关。我们表明，由于正交化的结果，特征选择方法保留了PCA中的特殊属性，即保留的方差可以表示为所保留的正交特征方差之和。

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