A New Paradigm for Classification Based on Dissimilarity Information via Regularized Kernel Estimation

基于相异信息的分类新范式 通过正则化核估计 格雷斯Wahba，PI本研究的目的是开发改进的方法进行分类和聚类时，属性向量的对象的兴趣是未知的或或一个更高的维度比是有用的，但当对对象之间的相异信息是可用的。在所提出的工作中，这种相异性信息可能是主观的、粗糙的、嘈杂的、不完整的、局限于非线性流形内的、来自多个来源的和/或不一致的。该方法是建立在PI和合作者的一些初步工作的基础上，他们提出了两种新的鲁棒非参数方法来获得正定核（也称为“再生核”）。这些内核生成“伪属性”向量，其可用于聚类、离群值检测，或在具有丰富标记数据的支持向量机中，或在具有稀疏标记数据（“半监督学习”）的支持向量机中用于分类。任务提出了建立一系列优化的分类系统下的各种科学上重要的情况下，关于可用的数据的性质，其中联合收割机强大的估计内核与支持向量机的基础上相异性信息的分类效果。建议为这些系统开发理论上有效和实际上有用的优化程序和高效算法，在精心设计的测试床中测试结果，其中答案是已知的，将其应用于各种不同的分类任务，将结果与相关系统进行比较，并公布结果。现代分类工具在语音识别、文本分类、图像分析、蛋白质和微阵列数据分类等方面做得令人印象深刻。然而，在某些方面仍有很大的改进空间。 这项工作将提供一个独特的和新颖的贡献，分类的理论和实践时，可用的数据可能是主观的，粗糙的，嘈杂的，不完整的，满足复杂的约束条件，来自多个来源，可能是不一致的。预计拟议的工作将提供改进的统计分析方法，这些方法有可能严重影响收集待分类数据的任何工程或科学奋进。

ABSTRACTA New Paradigm for Classification Based on Dissimilarity Information Via Regularized Kernel Estimation Grace Wahba, PIThe objective of this research is to develop improved methods for classification and clustering when attribute vectors for the objects of interest are either not known or or are of a much higher dimension than is useful, but when dissimilarity information between pairs of objects is available. In the work being proposed, this dissimilarity information may be subjective, crude, noisy, incomplete, confined within a nonlinear manifold, come from multiple sources and/or be inconsistent.The approach is to build on some preliminary work by the PI and collaborators, who have initiated two new robust nonparametric methods for obtaining positive definite kernels (a.k.a "reproducing kernels")from noisy dissimilarity data under various circumstances. These kernels generate "pseudo-attribute" vectors which may be used for clustering, for outlier detection, or in a support vector machine with copiously labeled data, or with sparsely labeled data ("semi-supervised learning") for classification. Tasks are proposed to build a series of optimized classification systems under a variety of scientifically important scenarios regarding the nature of the data available, which combine robustly estimated kernels with support vector machines to effect classification based on dissimilarity information. It is proposed to develop theoretically valid and practically useful optimization procedures and efficient algorithmsfor these systems, test the results in carefully designed test beds where the answer is known, apply them to a variety of different classification tasks, compare the results with related systems, and publicize the results.With the availability of extremely large amounts of data and high speed computing, modern classification tools are doing impressive things in speech recognition, text classification, image analysis, and classification of proteins and microarray data, among other things. However there is still much room for improvementin certain areas. This work will provide a unique and novel contribution to the theory and practice of classification when the data available may be subjective, crude, noisy, incomplete, satisfy complex constraints, come from multiple sources and may be inconsistent. It is anticipated that the proposed work will provide improved methods of statistical analysis that have the potential to seriously impact essentially any engineering or scientific endeavor that collects data to be classified.