高维数据降维技术及研究进展

降维技术旨在将高维数据映射到更低维的数据空间上以寻求数据紧凑表示，该技术有利于对数据做进一步处理。随着多媒体技术和计算机技术的高速发展，数据维度呈爆炸性增长，使得机器学习、图像处理等研究领域的数据分析变得为越来越困难。为消除上述问题造成的维度灾难，研究学者提出了一系列的解决方法。文中为探索这些降维技术的实用性，介绍了传统的降维技术以及近年推出的降维技术，分析了典型降维技术的性能，指出降维技术仍存在的问题并分析了未来值得关注的研究方向。

Research Progress of Dimensionality Reduction Technology on High-dimensional Data

Dimensionality reduction technologies is a technique designed to map high-dimensional data to a high-dimensional space to find a compact structure of data, which facilitates further processing of the data. With the rapid development of multimedia and computer technology, the data dimension grows explosively, which makes the image processing, machine learning and other areas of data analysis becomes increasingly difficult, in order to eliminate the problem of the dimension disaster, the researchers put forward a series of solutions. In order to explore the practicality of these dimensionality reduction technologies, this paper introduces the traditional dimensionality reduction technologies and the dimensionality reduction technologies in recent years, then analyzes the performance of the typical dimensionality reduction technologies. Finally, the problems that still exist in the dimensionality reduction technologies are pointed out and some suggestions about future research work are discussed.