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Supervised Learning of Quantizer Codebooks by Information Loss Minimization

机译:通过信息损失最小化监督量化器代码本的学习

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This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss such that the quantizer index of a given feature vector approximates a sufficient statistic for its class label. Informally, the quantized representation retains as much information as possible for classifying the feature vector correctly. We derive an alternating minimization procedure for simultaneously learning codebooks in the euclidean feature space and in the simplex of posterior class distributions. The resulting quantizer can be used to encode unlabeled points outside the training set and to predict their posterior class distributions, and has an elegant interpretation in terms of lossless source coding. The proposed method is validated on synthetic and real data sets and is applied to two diverse problems: learning discriminative visual vocabularies for bag-of-features image classification and image segmentation.
机译:本文提出了一种在最小化经验信息损失的基础上,对连续特征及其类别标签的后验分布进行联合量化的技术,从而使给定特征向量的量化指标近似于其类别标签的足够统计量。非正式地,量化表示保留了尽可能多的信息以正确分类特征向量。我们导出了一个交替的最小化过程,用于同时在欧几里得特征空间和后验分布的单纯形中学习码本。所得的量化器可用于对训练集外的未标记点进行编码,并预测其后类分布,并且在无损源编码方面具有出色的解释能力。该方法在合成数据集和真实数据集上得到验证,并被应用于两个不同的问题:学习用于特征包图像分类和图像分割的区分性视觉词汇。

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