The power of comparative reasoning
The power of comparative reasoning
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DOI：
10.1109/iccv.2011.6126527
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
J. Yagnik;Dennis W. Strelow;David A. Ross;Ruei-Sung Lin
中科院分区：
文献类型：
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作者：
J. Yagnik;Dennis W. Strelow;David A. Ross;Ruei-Sung Lin
秩相关测度以其对数值扰动的弹性而闻名，并广泛用于许多评价指标中。这样的顺序措施很少被应用在治疗的数字功能作为一个代表性的转换。我们强调的好处，序数表示的输入功能的理论和经验。我们提出了一个家庭的算法计算序嵌入的偏序统计量的基础上。除了具有有序度量的稳定性优势外，这些嵌入是高度非线性的，从而产生了几种机器学习方法非常青睐的稀疏特征空间。这些嵌入是确定性的，数据独立的，并且由于基于偏序统计，增加了对噪声的另一程度的弹性。这些无需机器学习的方法在应用于快速相似性搜索任务时，其性能优于具有复杂优化设置的最先进机器学习方法。为了解决分类问题，嵌入提供了一个非线性变换，从而产生了稀疏的二进制代码，非常适合于一大类机器学习算法。这些方法使用简单的线性分类器，可以快速训练VOC 2010显着改善。我们的方法可以扩展到多项式内核的情况下，同时允许非常有效的计算。此外，由于流行的最小哈希算法是我们的方法的一个特殊情况下，我们证明了一个有效的计划计算最小哈希的二进制特征的合取。实际的方法可以在大多数语言中用大约10行代码实现（MAT-LAB中为2行），并且不需要任何数据驱动的优化。
Rank correlation measures are known for their resilience to perturbations in numeric values and are widely used in many evaluation metrics. Such ordinal measures have rarely been applied in treatment of numeric features as a representational transformation. We emphasize the benefits of ordinal representations of input features both theoretically and empirically. We present a family of algorithms for computing ordinal embeddings based on partial order statistics. Apart from having the stability benefits of ordinal measures, these embeddings are highly nonlinear, giving rise to sparse feature spaces highly favored by several machine learning methods. These embeddings are deterministic, data independent and by virtue of being based on partial order statistics, add another degree of resilience to noise. These machine-learning-free methods when applied to the task of fast similarity search outperform state-of-the-art machine learning methods with complex optimization setups. For solving classification problems, the embeddings provide a nonlinear transformation resulting in sparse binary codes that are well-suited for a large class of machine learning algorithms. These methods show significant improvement on VOC 2010 using simple linear classifiers which can be trained quickly. Our method can be extended to the case of polynomial kernels, while permitting very efficient computation. Further, since the popular Min Hash algorithm is a special case of our method, we demonstrate an efficient scheme for computing Min Hash on conjunctions of binary features. The actual method can be implemented in about 10 lines of code in most languages (2 lines in MAT-LAB), and does not require any data-driven optimization.