CP Tensor Decomposition with Cannot-Link Intermode Constraints
CP Tensor Decomposition with Cannot-Link Intermode Constraints
复制标题
具有无法链接模间约束的 CP 张量分解
DOI：
10.1137/1.9781611975673.80
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jette Henderson†, Bradley A
中科院分区：
文献类型：
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作者：
Jette Henderson†, Bradley A
张量因式分解是一种应用于从气候建模到医学信息学的各种领域的方法。张量是捕获n个对象之间关系的n向数组。这些多向数组可以被分解，以研究数据中存在的潜在基础。张量因式分解带来的两个挑战是：1)由此产生的因素可能是噪声的，彼此高度重叠；2)它们可能无法映射到一个领域内的洞察力。然而，就收集标签或领域特定约束所需的时间和领域专业知识而言，整合监管以增加洞察力因素的数量可能代价高昂。为了应对这些挑战，我们引入了带不能链接互模式约束的CANDECOMP/PARAFAC(CP)张量分解(CP-CLIC)，这是一个实现简洁、多样、可解释因子的框架。这是通过逐步学习约束来实现的，这些约束在分解过程中用辅助信息进行验证。通过对模拟数据的实验和在医学信息学中的实际应用，我们展示了CP-CLIC在提取稀疏、多样和可解释因素方面的潜力。
Tensor factorization is a methodology that is applied in a variety of fields, ranging from climate modeling to medical informatics. A tensor is ann-way array that captures the relationship between n objects. These multiway arrays can be factored to study the underlying bases present in the data. Two challenges arising in tensor factorization are 1) the resulting factors can be noisy and highly overlapping with one another and 2) they may not map to insights within a domain. However, incorporating supervision to increase the number of insightful factors can be costly in terms of the time and domain expertise necessary for gathering labels or domain-specific constraints. To meet these challenges, we introduce CANDECOMP/PARAFAC (CP) tensor factorization with Cannot-Link Intermode Constraints (CP-CLIC), a framework that achieves succinct, diverse, interpretable factors. This is accomplished by gradually learning constraints that are verified with auxiliary information during the decomposition process. We demonstrate CP-CLIC's potential to extract sparse, diverse, and interpretable factors through experiments on simulated data and a real-world application in medical informatics.