← Back to papers
2010 NIPS NeurIPS 2010

Deep Coding Network

Abstract

This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that extends recent results for local coordinate coding. The two-layer approach can be easily generalized to deeper structures in a hierarchical multiple-layer manner. Empirically, it is shown that the deep coding approach yields improved performance in benchmark datasets.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — nonlinear approximation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Representation Learning
🐣 Hot Topic Early Bird — representation learning

Authors

Yuanqing Lin , Tong Zhang , Shenghuo Zhu , Kai Yu

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Core Methods > Embedding Learning Deep Learning > Architectures > Neural Networks Machine Learning > Core Methods > Feature Learning Deep Learning > Learning Types > Representation Learning

Keywords

representation learning feature learning sparse coding hierarchical representation nonlinear approximation deep architecture deep coding deep coding network hierarchical multiple-layer nonlinear functional approximation
Download PDF

Related papers

Link Discovery using Graph Feature Tracking 2010
Trading off Mistakes and Don't-Know Predictions 2010
A Novel Kernel for Learning a Neuron Model from Spike Train Data 2010
Decomposing Isotonic Regression for Efficiently Solving Large Problems 2010
Learning Kernels with Radiuses of Minimum Enclosing Balls 2010