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Activity Number:
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353
- SPEED: Statistical Learning and Data Science Speed Session 2, Part 2
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 10:30 AM to 11:15 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #307734
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Title:
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Semi-Supervised, Dynamic Class-Informative Feature Learning
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Author(s):
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Vincent Pisztora*
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Companies:
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Keywords:
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Dimension Reduction;
Representation Learning;
Semi-Supervised;
Neural Networks;
Deep Learning
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Abstract:
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All successful classification tasks depend critically on a representation of the data for which there exists a learnable function distinguishing the classes. Without such a “class-informative” set of features, classification is not possible. A methodology is proposed which provides non-linear, semi-supervised class-informative feature set learning using a novel loss function and a dynamic training scheme. In the semi-supervised case, this methodology is shown to improve classification performance by incorporating the structure of unlabeled observations into the learned feature map.
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Authors who are presenting talks have a * after their name.
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