论文标题
神经社区编码进行分类
Neural Neighborhood Encoding for Classification
论文作者
论文摘要
受果蝇嗅觉电路的启发，Fly Bloom滤光片[Dasgupta等，2018]能够有效地用单个通行证来汇总数据，并已用于新颖性检测。我们提出了一个新的分类器（用于二进制和多级分类），该分类器可以通过每级“飞绽放过滤器”有效地编码每个类别的不同本地社区。对测试数据的推断需要有效的{\ tt flyhash} [Dasgupta等，2017]操作，然后进行高维，但{\ Em稀疏}，dot product，dot tot to the class bloom滤光片。学习在微不足道上是可行的。在理论方面，我们建立了条件，在这些条件下，我们提出的分类器在任何测试示例中的预测都与概率很高的最近邻居分类器的预测一致。我们用超过50美元的数据集多种多样的数据维度进行了广泛评估我们所提出的方案，以证明我们提出的受神经科学启发的分类器的预测性能是最近的邻居分类器和其他单元分类器的竞争力。
Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [Dasgupta et al., 2018] is able to efficiently summarize the data with a single pass and has been used for novelty detection. We propose a new classifier (for binary and multi-class classification) that effectively encodes the different local neighborhoods for each class with a per-class Fly Bloom Filter. The inference on test data requires an efficient {\tt FlyHash} [Dasgupta, et al., 2017] operation followed by a high-dimensional, but {\em sparse}, dot product with the per-class Bloom Filters. The learning is trivially parallelizable. On the theoretical side, we establish conditions under which the prediction of our proposed classifier on any test example agrees with the prediction of the nearest neighbor classifier with high probability. We extensively evaluate our proposed scheme with over $50$ data sets of varied data dimensionality to demonstrate that the predictive performance of our proposed neuroscience inspired classifier is competitive the the nearest-neighbor classifiers and other single-pass classifiers.
