论文标题
procrustean正交稀疏哈希
Procrustean Orthogonal Sparse Hashing
论文作者
论文摘要
哈希是相似性搜索的最流行方法之一，因为它的速度和效率。密集的二元散列在文献中很普遍。最近，昆虫的嗅觉在结构和功能上类似于稀疏的哈希[6]。在这里，我们证明这种生物学机制是解决良好优化问题的解决方案。此外，我们表明正交性提高了稀疏哈西的准确性。接下来，我们提出了一种新颖的方法，即Procrustean正交稀疏哈希（POSH），该方法统一了这些发现，从与稀疏的哈希机制兼容的训练数据中学习正交转换。我们提供了最佳稀疏提升（OSL）[22]和Biohash [30]的理论证据，这是两种相关的嗅觉启发方法，并提出了两种新方法，即二进制OSL和SphericalHash，以解决这些不足。我们将POSH，Binary OSL和Spherichhash与几种最先进的哈希方法进行比较，并为所提出的方法优越的广泛标准基准和参数设置提供了经验结果。
Hashing is one of the most popular methods for similarity search because of its speed and efficiency. Dense binary hashing is prevalent in the literature. Recently, insect olfaction was shown to be structurally and functionally analogous to sparse hashing [6]. Here, we prove that this biological mechanism is the solution to a well-posed optimization problem. Furthermore, we show that orthogonality increases the accuracy of sparse hashing. Next, we present a novel method, Procrustean Orthogonal Sparse Hashing (POSH), that unifies these findings, learning an orthogonal transform from training data compatible with the sparse hashing mechanism. We provide theoretical evidence of the shortcomings of Optimal Sparse Lifting (OSL) [22] and BioHash [30], two related olfaction-inspired methods, and propose two new methods, Binary OSL and SphericalHash, to address these deficiencies. We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.
