Multi-Class Classification from Single-Class Data with Confidences
- URL: http://arxiv.org/abs/2106.08864v1
- Date: Wed, 16 Jun 2021 15:38:13 GMT
- Title: Multi-Class Classification from Single-Class Data with Confidences
- Authors: Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi
Sugiyama
- Abstract summary: We propose an empirical risk minimization framework that is loss-/model-/optimizer-independent.
We show that our method can be Bayes-consistent with a simple modification even if the provided confidences are highly noisy.
- Score: 90.48669386745361
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Can we learn a multi-class classifier from only data of a single class? We
show that without any assumptions on the loss functions, models, and
optimizers, we can successfully learn a multi-class classifier from only data
of a single class with a rigorous consistency guarantee when confidences (i.e.,
the class-posterior probabilities for all the classes) are available.
Specifically, we propose an empirical risk minimization framework that is
loss-/model-/optimizer-independent. Instead of constructing a boundary between
the given class and other classes, our method can conduct discriminative
classification between all the classes even if no data from the other classes
are provided. We further theoretically and experimentally show that our method
can be Bayes-consistent with a simple modification even if the provided
confidences are highly noisy. Then, we provide an extension of our method for
the case where data from a subset of all the classes are available.
Experimental results demonstrate the effectiveness of our methods.
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