论文标题
高阶同时张力通过面部分割的超图张子
Higher order co-occurrence tensors for hypergraphs via face-splitting
论文作者
论文摘要
计算成对共发生矩阵的流行技巧是入射矩阵及其转置的乘积。我们提出了一个使用面部分割产品或称为转置khatri-rao产品的高阶元组共发生的类似物。这些高阶同时出现在其他令牌公司中编码令牌的共同点，从而概括了常见研究的相互信息。我们通过流行的NLP模型和相似性的超图模型证明了这种张量的使用。
A popular trick for computing a pairwise co-occurrence matrix is the product of an incidence matrix and its transpose. We present an analog for higher order tuple co-occurrences using the face-splitting product, or alternately known as the transpose Khatri-Rao product. These higher order co-occurrences encode the commonality of tokens in the company of other tokens, and thus generalize the mutual information commonly studied. We demonstrate this tensor's use via a popular NLP model, and hypergraph models of similarity.
