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This technical report introduces some software packages for partial SVD computation, including optimized PROPACK, modified PROPACK for computing singular values above a threshold and the corresponding singular vectors, and block Lanczos with warm …

We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data.

We propose using the block Lanczos method to compute the partial SVDs, where the principal singular subspaces obtained in the previous iteration are used to start the block Lanczos procedure.

This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted.

We propose a framework for learning a system of PDEs from real data.

This paper studies algorithms for solving the problem of recovering a low-rank matrix with a fraction of its entries arbitrarily corrupted.

In this paper, we propose a framework for learning a system of PDEs from real data to accomplish a specific vision task.

We proposed a novel approach to approximate an area light source with a point light source for each component of the shading model.