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A classification algorithm for high-dimensional data

机译：一种高维数据的分类算法

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摘要

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.
机译：随着高维存储的大数据和流媒体数据的出现，突然的机器学习非常大的规模已经成为一个关键需求。这种机器学习应该非常快，应该用音量和维度轻松扩展，应该能够从流数据中学习，应该自动对高维数据进行维度降低，并应在硬件上部署。神经网络良好地定位以解决大规模机器学习的这些挑战。在本文中，我们提出了一种可以有效处理大规模，高维数据的方法。它是一种在线方法，可用于流媒体和大卷存储的大数据。它主要使用kohonen网，尽管只有来自多个kohonen网的少数选定的神经元（节点）实际上是保留的;我们在训练后丢弃所有Kohonen网。我们使用Kohonen网通过特征选择来减少维数，并使用单kohonen神经元构建分类器的集合。该方法旨在利用大规模的并行性，并且应在实现Kohonen网的硬件上轻松部署。提出了一些初始计算结果。

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