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

摘要基于正则化核估计的基于相异信息的分类新范式Grace Wahba，p本研究的目的是在感兴趣对象的属性向量未知、或具有比有用的维度高得多的维度时，但当对象对之间的相异信息可用时，开发改进的分类和聚类方法。在提出的工作中，这种相异信息可能是主观的、粗糙的、噪声的、不完全的、限制在非线性流形中、来自多个来源和/或不一致的。方法是建立在PI和合作者的一些前期工作的基础上，他们提出了两种新的健壮的非参数方法来从不同环境下的噪声相异数据中获得正定核(也称为“再生核”)。这些核生成可用于聚类、用于离群点检测、或在具有丰富标记数据的支持向量机中、或具有稀疏标记数据的支持向量机中用于分类的“伪属性”向量(“半监督学习”)。任务是在各种科学上具有重要意义的场景下构建一系列优化的分类系统，将稳健估计的核函数与支持向量机相结合来实现基于不同信息的分类。人们建议为这些系统开发理论上有效和实用的优化程序和高效算法，在精心设计的已知答案的试验台上测试结果，将它们应用于各种不同的分类任务，将结果与相关系统进行比较，并将结果公布。随着海量数据和高速计算的可用，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的分类等方面取得了令人印象深刻的成就。然而，在某些方面仍有很大的改进空间。当可获得的数据可能是主观的、粗糙的、有噪声的、不完整的、满足复杂约束、来自多个来源并且可能不一致时，这项工作将为分类理论和实践提供独特和新颖的贡献。预计拟议的工作将提供改进的统计分析方法，有可能严重影响收集数据进行分类的任何工程或科学工作。

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.