Manifold Recovery Using Kernel Low-Rank Regularization: Application to Dynamic Imaging

Kernel Locality Preserving Low-Rank Representation with Tikhonov Regularization

Classification based on Low-Rank Representation (LRR) has been a hot-topic in the field of pattern classification. However, LRR may not be able to fuse the local and global information of data completely and fail to represent nonlinear samples. In this paper, we propose a kernel locality preserving low-rank representation with Tikhonov regularization (KLP-LRR) for face recognition. KLP-LRR is a...

Robust Manifold Unfolding with Kernel Regularization

Low-Rank Tensor Regularization for Improved Dynamic Quantitative Magnetic Resonance Imaging

In certain medical imaging scenarios, a series of images that vary across organ motion and contrast changes are acquired. In such cases, the reconstruction amounts to recovering ≥ 4-dimensional images. Furthermore, due to the nature of organ motion and contrast changes, such datasets can be well-represented using low-rank tensors. In this work, we investigate the utility of low-rank tensor regu...

Manifold Based Low-Rank Regularization for Image Restoration and Semi-Supervised Learning

Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear appro...

Using Dynamic Kernel Instrumentation for Kernel and Application Tuning

We have designed a new technology, fine-grained dynamic instrumentation of commodity operating system kernels , which can insert runtime-generated code at almost any machine code instruction of an unmodified operating system kernel. This technology is ideally suited for kernel performance profiling, debugging, code coverage, runtime optimization, and extensibility. We have written a tool called...