14 search results - page 1 / 3 » Learning Low Rank Matrices from O(n) Entries |
CORR
2008
Springer
13 years 4 months ago
2008
Springer
How many random entries of an n
NIPS
2004
13 years 6 months ago
2004
We prove generalization error bounds for predicting entries in a partially observed matrix by fitting the observed entries with a low-rank matrix. In justifying the analysis appro...
SIAMJO
2011
12 years 11 months ago
2011
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
COLT
2005
Springer
13 years 10 months ago
2005
Springer
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generali...
AAAI
2012
11 years 7 months ago
2012
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...