Combining Dimensions and Features in Similarity-Based Representations
Combining Dimensions and Features in Similarity-Based Representations
复制标题
在基于相似性的表示中组合维度和特征
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
M. Lee
中科院分区：
文献类型：
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作者：
D. Navarro;M. Lee
提出了一种新的相似性数据表示模型，该模型将连续维度和离散特征相结合。描述了一种能够学习这些表示的算法，并开发了一种贝叶斯模型选择方法来选择适当数量的维度和特征。该方法在考虑数字0到9之间的相似性的经典数据集上进行了演示。
This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An algorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities between the numbers 0 through 9.