Heterogeneous transfer clustering contributes to improve the performance of target domain by using the co-occurrence data from different domains without any supervision. Existing works usually use a large of complete co-occurrence data to learn the projection functions mapping heterogeneous data to a common latent feature subspace. Given that in the real-world problems, complete and abundant co-occurrence data in the form of homogeneous transfer learning between the soured domain and target domain are hard to achieve, a heterogeneous transfer clustering method for partial co-occurrence data (HTCPC) is proposed here, to perform unsupervised learning to map the partial information obtained from the source domain onto objects in the target domain. Furthermore, to maximize the useful information to improve the clustering performance in target domain, the proposed HTCPC uses the deep matrix decomposition framework to maintain the multi-layer hidden feature representation and retain the complexity of the data hierarchy by adding the approximate orthogonal constraints, which can effectively strengthen the independence and minimal redundancy. From a series of experiments conducted on four datasets [Berkeley Drosophila Genome Project (BDGP), Devanagari Handwritten Character (DHC), Columbia University Image Library (COIL), and Notting-Hill (NH)], the results show that HTCPC outperforms the peers in the following aspects: our method constructs the hierarchical structure in the multi-layer latent representations and the proposed algorithm can reduce the redundancy and extract more useful knowledge for target domain.

中文翻译：

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部分共现数据的无监督异构迁移学习

异构迁移聚类通过在没有任何监督的情况下使用来自不同域的共现数据来提高目标域的性能。现有工作通常使用大量完整的共现数据来学习将异构数据映射到公共潜在特征子空间的投影函数。鉴于在现实世界的问题中，源域和目标域之间的同质迁移学习形式的完整和丰富的共现数据难以实现，部分共现数据的异构迁移聚类方法（HTCPC）是这里提出，执行无监督学习，将从源域获得的部分信息映射到目标域中的对象上。此外，为了最大化有用信息以提高目标域中的聚类性能，所提出的 HTCPC 使用深度矩阵分解框架通过添加近似正交约束来保持多层隐藏特征表示并保留数据层次结构的复杂性，可以有效地加强独立性和最小冗余。从对四个数据集 [伯克利果蝇基因组计划 (BDGP)、梵文手写字符 (DHC)、哥伦比亚大学图像库 (COIL) 和诺丁山 (NH)] 进行的一系列实验中，结果表明 HTCPC 优于同行在以下方面：我们的方法在多层潜在表示中构建了层次结构，并且所提出的算法可以减少冗余并为目标域提取更多有用的知识。