File Name: unsupervised feature learning and deep learning .zip
- Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors
- Unsupervised Feature Learning With Winner-Takes-All Based STDP
- Deep learning
Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets DBNs and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model HMM to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented.
Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Courville and P. Courville , P. The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data.
Although domain knowledge can be used to help design representations, learning can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Background Citations. Methods Citations. Results Citations. Figures and Topics from this paper.
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On deep generative models with applications to recognition. View 4 excerpts, references background and methods. Selecting Receptive Fields in Deep Networks. Algorithms for manifold learning.
Unsupervised Feature Learning With Winner-Takes-All Based STDP
Mining Intelligence and Knowledge Exploration pp Cite as. Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from non-adaptability to the particular dataset. To alleviate the problems of hand-crafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and PCA. We evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods.
Metrics details. Unfortunately, the nature of high dimension of neural data and few available samples led to the creation of a precise computer diagnostic system. Machine learning techniques, especially deep learning, have been considered as a useful tool in this field.
Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Improving deep convolutional neural networks with unsupervised feature learning Abstract: The latest generation of Deep Convolutional Neural Networks DCNN have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network CDBN has also achieved state-of-the-art in many computer vision tasks.