Robust Structured Subspace Learning for Data Representation
Abstract:To uncover an appropriate latent subspace for data representation, in this paper we propose a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image understanding and feature learning into a joint learning framework. The learned subspace is adopted as an intermediate space to reduce the semantic gap between the low-level visual features and the high-level semantics. To guarantee the subspace to be compact and discriminative, the intrinsic geometric structure of data, and the local and global structural consistencies over labels are exploited simultaneously in the proposed algorithm. Besides, we adopt the `2;1-norm for the formulations of loss function and regularization respectively to make our algorithm robust to the outliers and noise. An efficient algorithm is designed to solve the proposed optimization problem. It is noted that the proposed framework is a general one which can leverage several well-known algorithms as special cases and elucidate their intrinsic relationships. To validate the effectiveness of the proposed method, extensive experiments are conducted on diversity datasets for different image understanding tasks, i.e., image tagging, clustering, and classification, and the more encouraging results are achieved compared with some state-of-the-art approaches.
为了挖掘出一个合适的潜在子空间来进行数据表示，本文提出了一种新型的Robusted Structured Subspace Learning(RSSL)算法，将图像理解和特征学习整合到一个联合学习框架中。采用学习的子空间作为中间空间，以减少低级视觉特征与高级语义之间的语义差距。为了保证子空间的紧凑性和判别性，我们在算法中同时利用了数据的内在几何结构，以及标签的局部和全局结构一致性。此外，我们采用了损失函数和正则化的2;1常数，使我们的算法对离群值和噪声有很好的抵抗能力。构建了一种高效的算法来解决所提出的优化问题。值得注意的是，所提出的框架是一个通用的框架，它可以利用几种著名的算法作为特殊情况，并阐明它们的内在关系。为了验证所提出的方法的有效性，在不同的图像理解任务，即图像标记、聚类和分类的多样性数据集上进行了大量的实验，与一些最先进的方法相比，取得了比较令人鼓舞的结果。