%A JIAN Song-lei, LU Kai %T Survey on Representation Learning of Complex Heterogeneous Data %0 Journal Article %D 2020 %J Computer Science %R 10.11896/jsjkx.190600180 %P 1-9 %V 47 %N 2 %U {https://www.jsjkx.com/CN/abstract/article_18860.shtml} %8 2020-02-15 %X With the coming of the eras of artificial intelligence and big data,various complex heterogeneous data emerge continuously,becoming the basis of data-driven artificial intelligence methods and machine learning models.The quality of data representation directly affects the performance of following learning algorithms.Therefore,it is an important research area for representing useful complex heterogeneous data for machine learning.Firstly,multiple types of data representations were introduced and the challenges of representation learning methods were proposed.Then,according to the data modality,the data were categorized into singe-type data and multi-type data.For single-type data,the research development and typical representation learning algorithms for categorical data,network data,text data and image data were introduced respectively.Further,the multi-type data compounded by multiple single-type data were detailed,including the mixed data containing both categorical features and continuous features,the attributed network data containing node content and topological network,cross-domain data derived from different domains and the multimodal data containing multiple modalities.And based on these data,the research development and state-of-the-art representation learning models were introduced.Finally,the development trends on representation learning of complex heterogeneous data were discussed.