IEEE Trans Cybern. 2014 Jun;44(6):936-49. doi: 10.1109/TCYB.2013.2277664. Epub 2013 Oct 18.
A novel embedding-based dimensionality reduction approach, called structural Laplacian Eigenmaps, is proposed to learn models representing any concept that can be defined by a set of multivariate sequences. This approach relies on the expression of the intrinsic structure of the multivariate sequences in the form of structural constraints, which are imposed on dimensionality reduction process to generate a compact and data-driven manifold in a low dimensional space. This manifold is a mathematical representation of the intrinsic nature of the concept of interest regardless of the stylistic variability found in its instances. In addition, this approach is extended to model jointly several related concepts within a unified representation creating a continuous space between concept manifolds. Since a generated manifold encodes the unique characteristic of the concept of interest, it can be employed for classification of unknown instances of concepts. Exhaustive experimental evaluation on different datasets confirms the superiority of the proposed methodology to other state-of-the-art dimensionality reduction methods. Finally, the practical value of this novel dimensionality reduction method is demonstrated in three challenging computer vision applications, i.e., view-dependent and view-independent action recognition as well as human-human interaction classification.
提出了一种基于嵌入的新型降维方法，称为结构拉普拉斯特征映射，用于学习表示任何可以由一组多元序列定义的概念的模型。该方法依赖于多元序列内在结构的表达形式，以结构约束的形式施加于降维过程中，以在低维空间中生成紧凑且数据驱动的流形。这个流形是所关注概念的内在性质的数学表示，而与实例中的风格可变性无关。此外，该方法被扩展为在统一表示中联合建模几个相关概念，在概念流形之间创建连续空间。由于生成的流形编码了所关注概念的独特特征，因此可以用于对概念的未知实例进行分类。在不同数据集上的详尽实验评估证实了所提出方法相对于其他最先进的降维方法的优越性。最后，在三个具有挑战性的计算机视觉应用中证明了这种新型降维方法的实际价值，即依赖于视图和不依赖于视图的动作识别以及人与人之间的交互分类。
