Although the least-squares regression (LSR) has achieved great success in regression tasks, its discriminating ability is limited since the margins between classes are not specially preserved. To mitigate this issue, ...
详细信息
Although the least-squares regression (LSR) has achieved great success in regression tasks, its discriminating ability is limited since the margins between classes are not specially preserved. To mitigate this issue, dragging techniques have been introduced to remodel the regression targets of LSR. Such variants have gained certain performance improvement, but their generalization ability is still unsatisfactory when handling real data. This is because structure-related information, which is typically contained in the data, is not exploited. To overcome this shortcoming, in this article, we construct a multioutput regression model by exploiting the intraclass correlations and input-output relationships via a structure matrix. We also discriminatively enlarge the regression margins by embedding a metric that is guided automatically by the training data. To better handle such structured data with ordinal labels, we encode the model output as cumulative attributes and, hence, obtain our proposed model, termed structure-exploiting discriminative ordinal multioutput regression (SEDOMOR). In addition, to further enhance its distinguishing ability, we extend the SEDOMOR to its nonlinear counterparts with kernel functions and deep architectures. We also derive the corresponding optimization algorithms for solving these models and prove their convergence. Finally, extensive experiments have testified the effectiveness and superiority of the proposed methods.
In this paper we present an extended and improved algorithm based on our previous work for simulating the mechanical behavior of super carbon nanotubes (SCNTs). On previously considered level 0 SCNTs the performance i...
详细信息
ISBN:
(纸本)9781467382977
In this paper we present an extended and improved algorithm based on our previous work for simulating the mechanical behavior of super carbon nanotubes (SCNTs). On previously considered level 0 SCNTs the performance is improved by a factor higher than 2 when running in serial and a factor up to 4.4 when running in parallel on a 16 core SMP system. A new preprocessing step exploiting structural symmetry and an improved proximity-aware Matrix-Vector-Multiplication routine make this performance improvement possible while only consuming few additional memory. We also extend our symmetry considerations to SCNTs of order 1 and give an insight into the graph algebra based construction of these structures. Experimental results show that our new solver outperforms a compressed-row-storage based reference solver, on order 0 and 1 SCNTs, with and without deformations, while requiring only half the memory.
暂无评论