Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (Ac-tiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN). In DHNE, we introduce a novel semi-supervised heterogeneous network embedding method based on graph convolutional neu-ral network. In AQHN, we first introduce three active selection strategies based on uncertainty and representativeness, and then derive a batch selection method that assembles these strategies using a multi-armed bandit mechanism. ActiveHNE aims at improving the performance of HNE by feeding the most valuable supervision obtained by AQHN into DHNE. Experiments on public datasets demonstrate the effectiveness of ActiveHNE and its advantage on reducing the query cost.
translated by 谷歌翻译
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance. In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple rela-tional data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.
translated by 谷歌翻译
多聚类旨在探索替代聚类,从不同角度将数据组织成有意义的组。现有的多聚类算法是针对单视图数据而设计的。我们假设可以利用多视图数据的个性和通用性来生成高质量和多样化的聚类。为此,我们提出了一种新的多视图多聚类(MVMC)算法。 MVMC首先采用多视图自身表示学习来探索个性化编码矩阵和多视图数据的共享通用矩阵。它还使用希尔伯特 - 施密特独立准则(HSIC)减少了矩阵之间的冗余(即,增强个性),并通过强制共享矩阵在所有视图中平滑来收集共享信息。然后,它使用单个矩阵的矩阵因子以及共享矩阵,生成高质量的多样化聚类。我们进一步扩展了多视图数据的多聚类,并提出了一种称为多视图多聚共聚(MVMCC)的解决方案。我们的实证研究表明,MVMC(MVMCC)canexploit多视图数据可以生成多个高质量和多样化的聚类(共聚类),具有优于最先进方法的性能。
translated by 谷歌翻译
跨模式散列因其低模型数据检索的低存储成本和快速查询速度而受到越来越多的关注。然而,mostexisting散列方法是基于对象的手工制作或原始级别特征,这些特征可能与编码过程不是最佳兼容。此外,这些散列方法主要用于处理简单的双重相似性。与多个标签相关联的实例的复杂多级排序语义结构尚未得到很好的探索。在本文中,我们提出了一种基于排序的深度跨模态哈希方法(RDCMH)。 RDCM首先使用数据的特征和标签信息来导出asemi监督的语义排序列表。接下来,为了扩展手工制作特征的语义表示能力,RDCMH将语义分析信息集成到深度跨模态散列中,并联合优化深度特征表示和散列函数的兼容参数。实际多模态数据集的实验表明,RDCMH优于其他竞争对手基线并实现最先进的性能跨模式检索应用程序。
translated by 谷歌翻译
多集群旨在发现组织数据集群的各种方式。尽管取得了进展,但用户分析和理解每个输出聚类的独特结构仍然是一个挑战。在这个过程中,我们考虑嵌入在不同子空间中的不同聚类,并分析嵌入子空间以阐明每个聚类的结构。为此,我们提供了一个称为MISC(多个独立子空间聚类)的两阶段方法。在第一阶段,MISC使用独立子空间分析来寻找多个统计独立(即非冗余)子空间,并通过最小描述长度原理确定子空间的数量。在第二阶段,为了考虑嵌入在每个子空间中的样本的固有几何结构,MISC执行图正则化半非负矩阵分解以探索聚类。它还将内核技巧集成到矩阵分解中,以处理非线性可分离的集群。合成数据集的实验结果表明,MISC可以从独立的子空间中找到不同的有趣聚类,并且在实际数据集上也优于其他相关和竞争方法。
translated by 谷歌翻译
国际指纹活体检测竞赛(LivDet)是学术界和私人公司的开放和公认的交汇点,它处理区分来自人造材料和图像相对于真实指纹的指纹再现的图像的问题。在本期LivDet中,我们邀请竞争对手提出具有匹配系统的集成算法。目标是调查这种整合对整体绩效的影响程度。提交了12个算法,其中8个在集成系统上运行。
translated by 谷歌翻译