Metric learning as a service with covariance embedding

IM Kamal, H Bae, L Liu - IEEE Transactions on Services …, 2023 - ieeexplore.ieee.org
Metric learning as a service (MLaaS) represents one of the main learning streams to handle
complex datasets in service computing research communities and industries. A common
approach for dealing with high-dimensional and complex datasets is employing a feature
embedding algorithm to compress data through dimension reduction while optimizing intra-
class distance. To create generalizable MLaaS for high-performance artificial intelligence
applications with high-dimensional Big Data, a robust and meaningful embedding space …

Metric Learning as a Service with Covariance Embedding

I Mustafa Kamal, H Bae, L Liu - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
With the emergence of deep learning, metric learning has gained significant popularity in
numerous machine learning tasks dealing with complex and large-scale datasets, such as
information retrieval, object recognition and recommendation systems. Metric learning aims
to maximize and minimize inter-and intra-class similarities. However, existing models mainly
rely on distance measures to obtain a separable embedding space and implicitly maximize
the intra-class similarity while neglecting the inter-class relationship. We argue that to …
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