张量对象的随机投影建模

Ryohei Yokobayashi, T. Miura
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引用次数: 2

摘要

在本研究中,我们讨论了用于有效信息检索的高阶数据结构(称为张量),并特别展示了在保持信息之间欧几里得距离的同时,降维技术的效果如何。高阶数据结构需要大量的空间。其中一种有效的降维方法是潜在语义索引(LSI)和随机投影(RP),它们可以显著降低时间和空间的复杂性。这种约简技术可以应用于高阶数据结构。本文研究了高阶随机投影(HORP)算法,它能在保持可行降维的情况下提供有效的信息检索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling random projection for tensor objects
In this investigation, we discuss high order data structure (called tensor) for efficient information retrieval and show especially how well reduction techniques of dimensionality goes while preserving Euclid distance between information. High order data structure requires much amount of space. One of the effective approaches comes from dimensionality reduction such as Latent Semantic Indexing (LSI) and Random Projection (RP) which allows us to reduce complexity of time and space dramatically. The reduction techniques can be applied to high order data structure. Here we examine High Order Random Projection (HORP) which provides us with efficient information retrieval keeping feasible dimensionality reduction.
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