判别式
概率逻辑
模式识别(心理学)
计算机科学
人工智能
财产(哲学)
规范(哲学)
噪音(视频)
不确定性传播
机器学习
数学
图像(数学)
算法
哲学
认识论
政治学
法学
作者
Zhao Zhang,Li Zhang,Mingbo Zhao,Weiming Jiang,Yuchen Liang,Fanzhang Li
标识
DOI:10.1145/2671188.2749292
摘要
This paper proposes an enhanced semi-supervised classification approach termed Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the existing neighborhood propagation due to the fact that the outputted soft labels of points cannot be ensured to be sufficiently sparse, discriminative, robust to noise and be probabilistic values. Note that the sparse property and strong discriminating ability of predicted labels is important, since ideally the soft label of each sample should have only one or few positive elements (that is, less unfavorable mixed signs are included) deciding its class assignment. To reduce the negative effects of unfavorable mixed signs on the learning performance, we regularize the l2,1-norm on the soft labels during optimization for enhancing the prediction results. The non-negativity and sum-to-one constraints are also included to ensure the outputted labels are probabilistic values. The proposed framework is solved in an alternative manner for delivering a more reliable solution so that the accuracy can be improved. Simulations show that satisfactory results can be obtained by the proposed SparseNP compared with other related approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI