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The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns ...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth
Multivariate quantile quantile regression halfspace depth
2010/3/10
A new multivariate concept of quantile, based on a directional
version of Koenker and Bassett’s traditional regression quantiles, is
introduced for multivariate location and multiple-output regressi...
Discussion of “Multivariate quantiles and multiple-output regression quantiles：From L1 optimization to halfspace depth”
Multivariate quantiles multiple-output regression quantiles L1 optimization halfspace depth
2010/3/10
First I would like to congratulate the authors for developing a new concept
of directional quantile contours. The work will contribute well to the pursuit
of multivariate quantiles. The multiple out...
Feature Extraction for Universal Hypothesis Testing via Rank-constrained Optimization
Universal test mismatched universal test hypothesistesting feature extraction exponential family
2010/3/9
This paper concerns the construction of universal
tests for binary hypothesis testing, in which the alternate hypothesis
is poorly modeled and the observation space is large.
The mismatched univers...
Snell's optimization problem for sequences of convex compact valued random sets
Snell's optimization problem sequences of convex valued random sets
2009/9/21
A random set analogue of the Snell problem is presented.
In the original Snell's problem one observes a sequence of random
variables (t,), say a gambler's capital at successive games. If the gambler...
Portfolio Optimization with Non-Constant Volatility and Partial Information
Portfolio Optimization Non-Constant Volatility Partial Information
2009/9/17
Portfolio Optimization with Non-Constant Volatility and Partial Information。
Best subset selection，persistence in high-dimensional statistical learning and optimization under L1 constraint
Variable selection persistence
2010/4/26
Let (Y,X1, . . . ,Xm) be a random vector. It is desired to predict Y
based on (X1, . . . ,Xm). Examples of prediction methods are regression,
classification using logistic regression or separating h...