rank approximation
Towards a Zero-One Law for Column Subset Selection
Zhao Song, David Woodruff, Peilin Zhong
Indeed, this is a central question of recent workstudyinggeneralized lowrankmodels.
- Europe > Switzerland > Zürich > Zürich (0.14)
- North America > United States > Virginia > Arlington County > Arlington (0.04)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- (4 more...)
Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss
Zhao Song, David Woodruff, Peilin Zhong
Nevertheless, we show that under certain minimal and realistic distributional settings, it is possible to obtain a (1+ null)-approximation with a nearly linear running time and poly (k/null) + O ( k log n) columns. Namely, we show that if the input matrix A has the form A = B + E, where B is an arbitrary rank-k matrix, and E is a matrix with i.i.d.
- South America > Paraguay > Asunción > Asunción (0.04)
- North America > United States > Virginia > Arlington County > Arlington (0.04)
- North America > United States > Rhode Island > Providence County > Providence (0.04)
- (5 more...)
Regularized Weighted Low Rank Approximation
Frank Ban, David Woodruff, Richard Zhang
Neural Information Processing Systems http://nips.cc/
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > California > Alameda County > Berkeley (0.04)
- (2 more...)
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K. Ravikumar
Neural Information Processing Systems http://nips.cc/
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Wisconsin > Dane County > Madison (0.04)
- North America > United States > Illinois > Cook County > Chicago (0.04)
- (4 more...)
Approximation Algorithms for $\ell_0$-Low Rank Approximation
Karl Bringmann, Pavel Kolev, David Woodruff
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > San Francisco County > San Francisco (0.14)
- North America > United States > California > Alameda County > Berkeley (0.04)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- (10 more...)
Dimensionality Reduction of Massive Sparse Datasets Using Coresets
Dan Feldman, Mikhail Volkov, Daniela Rus
Neural Information Processing Systems http://nips.cc/
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.14)
- North America > United States > California > Los Angeles County > Los Angeles (0.14)
- Asia > Middle East > Israel > Haifa District > Haifa (0.04)
- (2 more...)
Regularized Weighted Low Rank Approximation
Frank Ban, David Woodruff, Richard Zhang
Neural Information Processing Systems http://nips.cc/
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > District of Columbia > Washington (0.04)
- (3 more...)
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K. Ravikumar
Neural Information Processing Systems http://nips.cc/
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Wisconsin > Dane County > Madison (0.04)
- North America > United States > Illinois > Cook County > Chicago (0.04)
- (4 more...)
Dynamic Tensor Product Regression Aravind Reddy Zhao Song Lichen Zhang
In this work, we initiate the study of Dynamic T ensor Product Regression .
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > Maryland > Baltimore (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > Afghanistan > Parwan Province > Charikar (0.04)
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David Woodruff
We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices.
- North America > Canada (0.14)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Minnesota (0.04)
- (5 more...)