Joint Representative Selection and Feature Learning: A Semi-Supervised Approach

Existing research suggests that adults and older children experience a tradeoff where instruction and feedback help them solve a problem efficiently, but lead them to ignore currently irrelevant information that might be useful in the future. It is unclear whether young children experience the same tradeoff. Eighty-seven children (ages five- to eight-years) and 42 adults participated in supervised feature prediction tasks either with or without an instructional hint. Follow-up tasks assessed learning of feature correlations and feature frequencies. Younger children tended to learn frequencies of both relevant and irrelevant features without instruction, but not the diagnostic feature correlation needed for the prediction task. With instruction, younger children did learn the diagnostic feature correlation, but then failed to learn the frequencies of irrelevant features. Instruction helped older children learn the correlation without limiting attention to frequencies. Adults learned the diagnostic correlation even without instruction, but with instruction no longer learned about irrelevant frequencies. These results indicate that young children do show some costs of learning with instruction characteristic of older children and adults. However, they also receive some of the benefits. The current study illustrates just what those tradeoffs might be, and how they might change over development.

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ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

IEEE Access ◽

10.1109/access.2021.3064819 ◽

2021 ◽

Vol 9 ◽

pp. 40511-40520

Author(s):

Chathurika S. Wickramasinghe ◽

Daniel L. Marino ◽

Milos Manic

Keyword(s):

High Dimensional Data ◽

Feature Learning ◽

Performance Degradation ◽

High Dimensional ◽

Unsupervised Feature Learning

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Joint Deep Networks based Multi-source Feature Learning for QoS Prediction

IEEE Transactions on Services Computing ◽

10.1109/tsc.2021.3050178 ◽

2021 ◽

pp. 1-1

Author(s):

Youhao Xia ◽

Ding Ding ◽

Zhenhua Chang ◽

Fan Li

Keyword(s):

Feature Learning ◽

Qos Prediction ◽

Deep Networks

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Construction of a deep sparse filtering network for rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

2021 ◽

pp. 095440702110148

Author(s):

Chun Cheng ◽

Wei Zou ◽

Weiping Wang ◽

Michael Pecht

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Back Propagation Neural Network ◽

Rotating Machinery ◽

Fine Tuning ◽

Local Optimum ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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