2014-2016, Image Hashing Theories and Methods Based on Data Dimensionality Reduction and Compressive Sensing, National Natural Science Foundation of China (NSFC), PI.
基于数据降维和压缩感知的图像哈希理论与方法，国家自然科学基金（青年科学基金项目）， 2014-2016，主持。
Most of the real-world data, such as images and videos, are always represented by tensor and high-dimensionality form, which make tensor data dimensionality reduction the hot issue in recent research.
实际应用中的许多数据，如图像，视频，通常具有张量性和高维性特征，张量数据的维数约简便成为近期的研究热点。
The algorithm is impractical on large data sets, unless it USES dimensionality reduction, sampling, or partitioning - all of which reduce recommendation quality.
在大数据集的情况下，这样的算法不可行，除非使用维度降低、抽样或区隔——所有这些都降低了推荐的品质。
Multidimensional scaling is a powerful tool for dimensionality reduction in the field of pattern recognition and data mining.
多维尺度分析是模式识别与数据挖掘领域一个有力的降维工具。
One of the steps of data preprocessing is dimensionality reduction.
维数约简是数据预处理的步骤之一。
Dimensionality reduction algorithms, as the key technologies of data preprocessing in intelligent recognition, have been used successfully recently.
近年来降维方法作为智能识别中关键的数据预处理技术得到了较为成功地运用。
Manifold learning attempts to obtain the intrinsic structure of non-linearly distributed data, which can be used in non-linear dimensionality reduction(NLDR).
流形学习旨在获得非线性分布数据的内在结构，可以用于非线性降维。
Due to the supervised view of point, most of the present tensor dimensionality reduction methods cannot take full advantage of the unlabeled data.
现有的张量维数约简方法大都是监督的，它们不能有效利用未标签样本数据的信息。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
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