欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
特征迁移重在领域共有特征间学习，然而其忽略领域特有特征的判别信息，使算法的适应性受到一定的局限．针对此问题，提出了一种融合异构特征的子空间迁移学习（ThesubspacetransferlearningMgorithmintegratingwithheterogeneousfeatures，STL—IHF）算法．该算法将数据的特征空间看成共享和特有两个特征子空间的组合，同时基于经验风险最小框架将共享特征和特有特征共同嵌入到支持向量机（Supportvectormachine，SVM）的训练过程中．其在共享特征子空间上实现知识迁移的同时兼顾了领域特有的异构信息，增强了算法的适应性．模拟和真实数据集上的实验结果表明了所提方法的有效性．
英文摘要：
The traditional transfer feature algorithms usually focus on learning by using the common features between the source domain and the target domain but ignore the discriminant information of the specific features of each domain, which makes the existing algorithms lack the adaptability to some extent. In order to circumvent this issue, in this paper a novel subspace transfer learning algorithm integrating heterogeneous features （STL-IHF） is proposed based on the empirical risk minimum framework. The proposed method is based on the support vector machine （SVM）-like framework with the feature space of each domain as a combination of the common features and the specified features. The proposed algorithm can not only realize the transfer learning from the common features but also effectively leverage the specified features of each domain, which makes it have much better adaptability in learning. Experimental results on simulation and real data set show the power of the proposed algorithm.
同期刊论文项目
同项目期刊论文
Dm-KDE: dynamical kernel density estimation by sequences of KDE estimators with fixed number of comp
An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Dat
T2FELA: type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic
Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neu
Minimax probability TSK fuzzy system classifier: a more transparent and highly interpretable classif
Takagi-Sugeno-Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic el
Feedforward kernel neural networks, generalized least learning machine, and its deep learning with a
Affinity and Penalty Jointly Constrained Spectral Clustering with All-Compatibility, Flexibility, an
期刊信息
位置：