Training a HyperDimensional Computing Classifier using a Threshold on its Confidence

Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things (IoT) devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This article proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples, but also samples that are correctly classified by the HDC model but with low confidence. As such, a confidence threshold is introduced that can be tuned for each dataset to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET and HAND dataset for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift towards higher confidence values of the correctly classified samples making the classifier not only more accurate but also more confident about its predictions

Prototype-based analysis of GAMA galaxy catalogue data

We present a prototype-based machine learning analysis of labeled galaxy catalogue data containing parameters from the Galaxy and Mass Assembly (GAMA) survey. Using both an unsupervised and supervised method, the Self-Organizing Map and Generalized Relevance Matrix Learning Vec- tor Quantization, we find that the data does not fully support the popular visual-inspection-based galaxy classification scheme employed to categorize the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. In a proof-of-concept experiment, we present the galaxy parameters that are most discriminative for this class