Automatic Generation of Co-Embeddings from Relational Data with Adaptive Shaping

作者：Mu Tingting^*; Goulermas John Yannis

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2340-2356.

DOI：10.1109/TPAMI.2013.66

• Relational data
• data co-embedding
• heterogeneous embedding
• data visualization
• structural matching

In this paper, we study the co-embedding problem of how to map different types of patterns into one common low-dimensional space, given only the associations (relation values) between samples. We conduct a generic analysis to discover the commonalities between existing co-embedding algorithms and indirectly related approaches and investigate possible factors controlling the shapes and distributions of the co-embeddings. The primary contribution of this work is a novel method for computing co-embeddings, termed the automatic co-embedding with adaptive shaping (ACAS) algorithm, based on an efficient transformation of the co-embedding problem. Its advantages include flexible model adaptation to the given data, an economical set of model variables leading to a parametric co-embedding formulation, and a robust model fitting criterion for model optimization based on a quantization procedure. The secondary contribution of this work is the introduction of a set of generic schemes for the qualitative analysis and quantitative assessment of the output of co-embedding algorithms, using existing labeled benchmark datasets. Experiments with synthetic and real-world datasets show that the proposed algorithm is very competitive compared to existing ones.

• 出版日期2013-10