Scalable Generative Models for Multi-label Learning with Missing Labels

@inproceedings{Jain2017ScalableGM,
  title={Scalable Generative Models for Multi-label Learning with Missing Labels},
  author={Vikas Jain and Nirbhay Modhe and Piyush Rai},
  booktitle={ICML},
  year={2017}
}
We present a scalable, generative framework for multi-label learning with missing labels. Our framework consists of a latent factor model for the binary label matrix, which is coupled with an exposure model to account for label missingness (i.e., whether a zero in the label matrix is indeed a zero or denotes a missing observation). The underlying latent factor model also assumes that the low-dimensional embeddings of each label vector are directly conditioned on the respective feature vector of… CONTINUE READING
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