Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data
Frankfurt Institute for Advanced Studies（法兰克福高等研究所）；Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society（恩斯特·施特龙曼神经科学研究所（与马克斯·普朗克学会合作））；ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University（吉森大学医学院动物研究跨学科3Rs中心（ICAR3R））
We introduce a novel type of computationally efficient artificial neural network (ANN) called the rank similarity filter (RSF). RSFs can be used to both transform and classify nonlinearly separable datasets with many data points and dimensions. The weights of RSF are set using the rank orders of features in a data point, or optionally the 'confusion' adjusted ranks between features (determined from their distributions in the dataset). The activation strength of a filter determines its similarity to other points in the dataset, a measure related to cosine similarity. The activation of many RSFs maps samples into a new nonlinear space suitable for linear classification (the rank similarity transform (RST)). We additionally used this method to create the nonlinear rank similarity classifier (RSC), which is a fast and accurate multiclass classifier, and the nonlinear rank similarity probabilistic classifier (RSPC), which is an extension to the multilabel case. We evaluated the classifiers on multiple datasets and RSC was competitive with existing classifiers but with superior computational efficiency. Open-source code for RST, RSC and RSPC was written in Python using the popular scikit-learn framework to make it easily accessible. In future extensions the algorithm can be applied to specialised hardware suitable for the parallelization of an ANN (GPU) and a Spiking Neural Network (neuromorphic computing) with corresponding performance gains. This makes RSF a promising solution to the problem of efficient analysis of nonlinearly separable data.
A Universal Metric of Dataset Similarity for Cross-silo Federated Learning
Department of Biomedical Informatics, Columbia University, NY, USA（生物医学信息学系，哥伦比亚大学）；New York Genome Center, NY, USA（纽约基因中心）；Department of Computer Science, Columbia University, NY, USA（计算机科学系，哥伦比亚大学）
Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search
UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Eshelman School of Pharmacy, UNC Chapel Hill（北卡罗来纳大学教堂山分校埃谢尔曼药学院）；Department of Pharmacology, UNC Chapel Hill（北卡罗来纳大学教堂山分校药理学系）；Joint Department of Biomedical Engineering at UNC Chapel Hill and NCSU（北卡罗来纳大学教堂山分校与北卡罗来纳州立大学生物医学工程联合系）
