Active Ordinal Querying for Tuplewise Similarity Learning
Active Ordinal Querying for Tuplewise Similarity Learning
复制标题
用于元组相似性学习的主动序数查询
DOI：
10.1609/aaai.v34i04.5734
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
C. Rozell
中科院分区：
文献类型：
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作者：
Gregory H. Canal;Stefano Fenu;C. Rozell
许多机器学习任务，如聚类、分类和数据集搜索，都受益于将数据点嵌入到距离反映人类感知的相对相似性的空间中。构造这种嵌入的常见方式是向oracle请求三元组相似性查询，将两个对象相对于引用进行比较。这项工作将三元组查询推广到任意大小的元组查询，要求Oracle对多个对象进行引用排名，并引入了一种高效且强大的自适应选择方法InfoTuple，该方法使用一种新的方法来实现互信息最大化。我们表明，InfoTuple在各种元组大小下的性能超过了最先进的自适应三元组选择方法在合成测试和新的人类响应数据集上的性能，并根据经验证明了通过查询更大的元组而不是三元组来实现效率和查询一致性的显着提高。
Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans. A common way to construct such an embedding is to request triplet similarity queries to an oracle, comparing two objects with respect to a reference. This work generalizes triplet queries to tuple queries of arbitrary size that ask an oracle to rank multiple objects against a reference, and introduces an efficient and robust adaptive selection method called InfoTuple that uses a novel approach to mutual information maximization. We show that the performance of InfoTuple at various tuple sizes exceeds that of the state-of-the-art adaptive triplet selection method on synthetic tests and new human response datasets, and empirically demonstrate the significant gains in efficiency and query consistency achieved by querying larger tuples instead of triplets.