Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Electronic circuits: analysis, simulation, and design
Electronic circuits: analysis, simulation, and design
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Incomplete-data classification using logistic regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
On Classification with Incomplete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical evaluation of deep architectures on problems with many factors of variation
Proceedings of the 24th international conference on Machine learning
Quadratically gated mixture of experts for incomplete data classification
Proceedings of the 24th international conference on Machine learning
Learning from incomplete data with infinite imputations
Proceedings of the 25th international conference on Machine learning
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
Max-margin Classification of Data with Absent Features
The Journal of Machine Learning Research
Deep networks for image retrieval on large-scale databases
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A deep-learning model-based and data-driven hybrid architecture for image annotation
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Image recognition with incomplete data is a well-known hard problem in multimedia content analysis. This paper proposes a novel deep learning technique called semiconducting bilinear deep belief networks (SBDBN) by referencing human's visual cortex and intelligent perception. Inheriting from deep models, SBDBN simulates the laminar structure of human's cerebral cortex and the neural loop in human's visual areas. To address the special difficulties of image recognition with incomplete data, we design a novel second-order deep architecture with semiconducting restricted boltzmann machines. Moreover, two peaks activation of human's perception is implemented by three learning stages of semiconducting bilinear discriminant initialization, greedy layer-wise reconstruction, and global fine-tuning. Owing to exploiting the embedding information according to the reliable features rather than any completion of missing features, the proposed SBDBN has demonstrated outstanding recognition ability on two standard datasets and one constructed dataset, comparing with both incomplete image recognition techniques and existing deep learning models.