Mining Semantically Consistent Patterns for Cross-View Data
Mining Semantically Consistent Patterns for Cross-View Data
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DOI：
10.1109/tkde.2014.2313866
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发表时间：
2014-03
影响因子：
8.9
通讯作者：
Lei Zhang;Yao Zhao;Zhenfeng Zhu;Shikui Wei;Xindong Wu
中科院分区：
文献类型：
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作者：
Lei Zhang;Yao Zhao;Zhenfeng Zhu;Shikui Wei;Xindong Wu
在某些现实世界中，例如信息检索和数据分类，我们经常面临以下情况：相同的语义概念可以使用具有相似信息的不同视图来表达。因此，对于这些应用程序，如何获得跨视图数据的互补信息的互补信息，如何获得某种语义一致的模式（SCP）非常重要。但是，跨视图表示之间的异质性在挖掘SCP方面带来了重大挑战。在本文中，我们提出了一个通用框架，以发现跨视图数据的SCP。具体而言，首先提出了一种新型的同构相关冗余转化（IRRT），旨在在不同视图之间构建特征呈异态空间。 IRRT线性地将多个异质低级特征空间映射到高维冗余特征形态形态的空间，我们将其称为中级空间。因此，可以捕获更多来自不同观点的互补信息。此外，为了挖掘中级空间中同构表示之间的语义一致性，我们提出了一个新的基于相关的关节特征学习（CJFL）模型，以提取在特征iSomorphic数据上共享的独特高级语义子空间。因此，可以获得跨视图数据的SCP。三个数据集的全面实验证明了我们在分类和检索方面的优势。
In some real world applications, like information retrieval and data classification, we often are confronted with the situation that the same semantic concept can be expressed using different views with similar information. Thus, how to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views, is of great importance for those applications. However, the heterogeneity among cross-view representations brings a significant challenge on mining the SCP. In this paper, we propose a general framework to discover the SCP for cross-view data. Specifically, aiming at building a feature-isomorphic space among different views, a novel Isomorphic Relevant Redundant Transformation (IRRT) is first proposed. The IRRT linearly maps multiple heterogeneous low-level feature spaces to a high-dimensional redundant feature-isomorphic one, which we name as mid-level space. Thus, much more complementary information from different views can be captured. Furthermore, to mine the semantic consistency among the isomorphic representations in the mid-level space, we propose a new Correlation-based Joint Feature Learning (CJFL) model to extract a unique high-level semantic subspace shared across the feature-isomorphic data. Consequently, the SCP for cross-view data can be obtained. Comprehensive experiments on three data sets demonstrate the advantages of our framework in classification and retrieval.