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We introduce a novel type of computationally efficient artificial neuralnetwork (ANN) called the rank similarity filter (RSF). RSFs can be used to bothtransform and classify nonlinearly separable datasets with many data points anddimensions. The weights of RSF are set using the rank orders of features in adata point, or optionally the 'confusion' adjusted ranks between features(determined from their distributions in the dataset). The activation strengthof a filter determines its similarity to other points in the dataset, a measurerelated to cosine similarity. The activation of many RSFs maps samples into anew nonlinear space suitable for linear classification (the rank similaritytransform (RST)). We additionally used this method to create the nonlinear ranksimilarity classifier (RSC), which is a fast and accurate multiclassclassifier, and the nonlinear rank similarity probabilistic classifier (RSPC),which is an extension to the multilabel case. We evaluated the classifiers onmultiple datasets and RSC was competitive with existing classifiers but withsuperior computational efficiency. Open-source code for RST, RSC and RSPC waswritten in Python using the popular scikit-learn framework to make it easilyaccessible. In future extensions the algorithm can be applied to specialisedhardware suitable for the parallelization of an ANN (GPU) and a Spiking NeuralNetwork (neuromorphic computing) with corresponding performance gains. Thismakes RSF a promising solution to the problem of efficient analysis ofnonlinearly separable data.

Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data