Adaptively Learning the Crowd Kernel
Adaptively Learning the Crowd Kernel
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发表时间：
2011-05
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通讯作者：
Omer Tamuz;Ce Liu;Serge J. Belongie;Ohad Shamir;A. Kalai
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作者：
Omer Tamuz;Ce Liu;Serge J. Belongie;Ohad Shamir;A. Kalai
我们介绍了一种算法，在给定n个对象的情况下，仅从众包数据中学习所有n2个对象对的相似度矩阵。该算法对自适应选择的基于三元组的相对相似查询的响应进行采样。每个查询的形式都是“对象a更类似于b还是更类似于c？”并且在给定前面的响应的情况下被选择为信息量最大的。输出是将对象嵌入到欧几里得空间(如MDS)中；我们将其称为“群核”。支持向量机揭示了群核在许多领域中捕捉到的显著而微妙的特征，例如领带之间的“is条纹”和字母之间的“元音对辅音”。
We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object a more similar to b or to c?" and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the "crowd kernel." SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as "is striped" among neckties and "vowel vs. consonant" among letters.