Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.
Machine Learning for Galaxy Morphology Classification
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abstract
In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue.
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Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection
Quantum convolutional autoencoders are adapted for reconstruction-based anomaly detection on time-series data, with a bottleneck architecture suggested to outperform hierarchical ones on an exoplanet dataset.