Unsupervised Feature Learning And Deep Learning Pdf

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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.

Citation Type. Has PDF. Publication Type. More Filters. Deep Learning of Representations. Research Feed. Highly Influenced. View 3 excerpts, cites background and methods. Unsupervised representation learning based on the deep multi-view ensemble learning. View 1 excerpt, cites methods. Deep Learning of Representations: Looking Forward. View 2 excerpts, cites background. Image clustering based on deep sparse representations.

View 1 excerpt. View 1 excerpt, cites background. View 15 excerpts, cites background. View 1 excerpt, references background. View 3 excerpts, references methods and background. Highly Influential. View 4 excerpts, references background. Non-Local Manifold Tangent Learning. View 2 excerpts, references background. Unsupervised feature learning for audio classification using convolutional deep belief networks. Extracting and composing robust features with denoising autoencoders. The Manifold Tangent Classifier.

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.

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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.

Deep learning

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.

We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity STDP biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All WTA framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process.

Unsupervised Feature Learning With Winner-Takes-All Based STDP

Deep learning also known as deep structured learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised , semi-supervised or unsupervised.

In machine learning , feature learning or representation learning [1] is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

1 Response
  1. Mohammed C.

    PDF | This paper gives a review of the recent developments in deep learning and un-supervised feature learning for time-series problems.

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