2020/08/19 by K. P. Sinha, Kaushik Sinha, Sinha, Kaushik +2
Computer Science · Engineering · Mathematics · #Advanced Chemical Sensor Technologies #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2008.08685
arxiv created 2020/08/19 · openalex publication_date 2020/08/19 · arxiv updated 2020/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.