Universal embeddings for kernel machine classification

  title={Universal embeddings for kernel machine classification},
  author={Petros T. Boufounos and Hassan Mansour},
  journal={2015 International Conference on Sampling Theory and Applications (SampTA)},
Visual inference over a transmission channel is increasingly becoming an important problem in a variety of applications. In such applications, low latency and bit-rate consumption are often critical performance metrics, making data compression necessary. In this paper, we examine feature compression for support vector machine (SVM)-based inference using quantized randomized embeddings. We demonstrate that embedding the features is equivalent to using the SVM kernel trick with a mapping to a… CONTINUE READING
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