Calibrated Simplex Mapping Classification

• URL: http://arxiv.org/abs/2103.02926v1
• Date: Thu, 4 Mar 2021 10:18:22 GMT
• Title: Calibrated Simplex Mapping Classification
• Authors: Raoul Heese, Micha{\l} Walczak, Michael Bortz, Jochen Schmid
• Abstract summary: We propose a novel supervised multi-class/single-label classifier that maps training data onto a linearly separable latent space with a simplex-like geometry. For its solution we can choose suitable distance metrics in feature space and regression models predicting latent space coordinates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel supervised multi-class/single-label classifier that maps training data onto a linearly separable latent space with a simplex-like geometry. This approach allows us to transform the classification problem into a well-defined regression problem. For its solution we can choose suitable distance metrics in feature space and regression models predicting latent space coordinates. A benchmark on various artificial and real-world data sets is used to demonstrate the calibration qualities and prediction performance of our classifier.

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