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An adaptive and efficient dimensionality reduction algorithm for high-dimensional indexing

机译：高维索引的自适应高效降维算法

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摘要

The notorious "dimensionality curse" is a well-known phenomenon for any multidimensional indexes attempting to scale up to high dimensions. One well known approach to overcoming degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them is a challenging task. We present an adaptive multilevel mahalanobis-based dimensionality reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has three notable features compared to existing methods. First, it discovers elliptical clusters using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimensionality. An extensive performance study using both real and synthetic datasets was conducted, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently.
机译：对于任何试图扩展到高维的多维索引，臭名昭著的“维数诅咒”是众所周知的现象。一种克服因尺寸增加而导致的性能下降的众所周知的方法是在构造索引之前减小原始数据集的尺寸。但是，识别尺寸之间的相关性并有效地减小尺寸之间的关系是一项艰巨的任务。我们为高维索引提出了一种基于自适应多级基于马哈拉诺比斯的降维（MMDR）技术。与现有方法相比，我们的MMDR技术具有三个显着特征。首先，它仅使用低维子空间发现椭圆簇。其次，使用单个B + -树对不同轴系统中的数据点进行索引。第三，我们的技术在数据大小和维度方面具有高度可扩展性。使用真实和合成数据集进行了广泛的性能研究，结果表明我们的技术不仅可以实现更高的精度，而且还可以有效地处理查询。

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