CORR
2008
Springer
13 years 4 months ago
2008
Springer
Consider a 0
CORR
2008
Springer
13 years 4 months ago
2008
Springer
We consider a general class of regularization methods which learn a vector of parameters on the basis of linear measurements. It is well known that if the regularizer is a nondecr...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from th...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at ran...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
Nonnegative matrix factorization (NMF) was popularized as a tool for data mining by Lee and Seung in 1999. NMF attempts to approximate a matrix with nonnegative entries by a produ...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
Recent work has demonstrated that using a carefully designed sensing matrix rather than a random one, can improve the performance of compressed sensing. In particular, a welldesign...
AUTOMATICA
2008
13 years 4 months ago
2008
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equival...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
The problem of minimizing the rank of a matrix subject to linear equality constraints arises in applications in machine learning, dimensionality reduction, and control theory, and...
FSTTCS
2008
Springer
13 years 5 months ago
2008
Springer
We consider the problem of maintaining information about the rank of a matrix M under changes to its entries. For an n × n matrix M, we show an amortized upper bound of O(nω−1)...