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Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems. As rank minimization problems are often converted to nuclear norm minimization (NNM) problems, they have to be solved iteratively and each iteration requires computing a singular value decomposition (SVD).(More)
Many machine learning problems are solved by algorithms that involve eigenvalue decomposition (EVD) or singular value decomposition (SVD) in each iteration. Therefore, these algorithms suffer from the high computation cost of multiple EVD/SVDs. To relieve this issue, we introduce the block Lanczos method to replace the original exact EVD/SVD in each(More)
We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the(More)
In the past decades, exactly recovering the intrinsic data structure from corrupted observations, which is known as robust principal component analysis (RPCA), has attracted tremendous interests and found many applications in computer vision. Recently, this problem has been formulated as recovering a low-rank component and a sparse component from the(More)
Diffusion kurtosis imaging (DKI) is a recent MRI based method that can quantify deviation from Gaussian behavior using a kurtosis tensor. DKI has potential value for the assessment of neurologic diseases. Existing techniques for diffusion kurtosis imaging typically need to capture hundreds of MRI images, which is not clinically feasible on human subjects.(More)
With the development of concentrated wind power areas, new energy’s dispatching problems are more prominent with its fast expansion. However, we can maximize the utilization of wind power under power curtailment conditions by optimal wind power dispatching. The paper studies on the basic theories of wind power turbines, and analyses the power’s control and(More)
Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffusion tensors in our model makes it less computationally intensive to obtain a maximum a posteriori (MAP) estimation. An approximate solution to the problem is achieved efficiently(More)