Abstract
Motivated by Fisher divergence, in this paper we present a new set of information quantities which we refer to as gradient information. These measures serve as surrogates for classical information measures such as those based on logarithmic loss, Kullback-Leibler divergence, directed Shannon information, etc. in many data-processing scenarios of interest, and often provide significant computational advantage, improved stability, and robustness. As an example, we apply these measures to the Chow-Liu tree algorithm, and demonstrate remarkable performance and significant computational reduction using both synthetic and real data.
Original language | English (US) |
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Journal | Advances in Neural Information Processing Systems |
Volume | 32 |
State | Published - 2019 |
Event | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada Duration: Dec 8 2019 → Dec 14 2019 |
Bibliographical note
Publisher Copyright:© 2019 Neural information processing systems foundation. All rights reserved.