On Sparse Optimization Problems with Linear Inequality Constraints Based on Huber Loss and Capped-L1 Regularization 对多元线性回归中回归系数的估计问题,本文考虑了基于Huber损失和线性不等式约束的稀疏优化模型.首先,给出了稀疏优化的原问题,基于Capped-L1正则的松弛问题和基于约束... 梦达 田 - 《Pure Mathemati...
capped L1-norm, loss function References [1] CORINNA CORTES, VLADIMIR VAPNIK.Support-vector networks[J]. Machine learning, 1995, 20(3):273-297. http://biomet.oxfordjournals.org/external-ref?access_num=10.1007/BF00994018&link_type=DOI [2] JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin support...
R-CTSVM+: Robust capped L1-norm twin support vector machine with privileged information In the learning using privileged information paradigm, it is the basic assumption that if a small loss in the correcting space on the privileged data can be obtained, then a small loss in the decision space...
Although L1-norm based methods are more robust, they fail to obtain joint sparse projections which play a significant role in feature selection. To address this problem, Nie et al. [11] proposed robust feature selection (RFS). RFS employs an L2,1-norm based loss function as well as an L2...
The remarkably high numbers of wounds and viral load on scarred or infested developing honey bees may have caused significant weight loss and extensive injuries observed on the abdomen, wings, legs, proboscis and antennae of adult honey bees. Together, the survival of infested honey bees was ...
Moreover, SIVcpz Nef has acquired the ability to antagonize CPZ tetherin without the loss of anti- RCM tetherin activity, indicating that SIVcpz Nef has made the way through a gain-of-function evolution (Figure 4). To our knowledge, here we demonstrated that SIVcpz Nef is able to ...
More precisely, we combined L1-norm with capped L1-norm to represent the amount of information extracted by the filter and control regularization. In the process of pruning, the insignificant filters remove directly without any loss in the test accuracy, providing much slimmer and compact models ...
More precisely, we combined L1-norm with capped L1-norm to represent the amount of information extracted by the filter and control regularization. In the process of pruning, the insignificant filters remove directly without any loss in the test accuracy, providing much slimmer and compact models ...
Traditional dictionary learning methods use quadratic loss function which is known sensitive to outliers. Hence they could not learn the good dictionaries when outliers exist. In this paper, aiming at learning dictionaries resistant to outliers, we proposed capped l_1-norm based dictionary learning ...
Consequently, the loss of fluorescence in catfish cells incubated at 27C was most likely due to endocytosis and not shedding of the membrane-bound fluorescence to the medium. In addition, analysis ...Yang M.C.W., Harvey N.E., Cuchens M.A., and Buttke T.M. (1988). Pulse profile ...