Automatic semantic annotation of high-resolution optical satellite images is a task to assign one or several predefined semantic concepts to an image according to its content. The fundamental challenge arises from the...
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ISBN:
(纸本)9781509033324
Automatic semantic annotation of high-resolution optical satellite images is a task to assign one or several predefined semantic concepts to an image according to its content. The fundamental challenge arises from the difficulty of characterizing complex and ambiguous contents of the satellite images. To address this challenge, a diversity constrained joint multi-feature learning method is proposed to learn robust feature representations for annotating satellite images. The key motivation of our method is to make full use of the complementarity diversity information among the heterogeneous features in the learning process. Comprehensive experiments on an annotation dataset demonstrate the superiority and effectiveness of our method compared with baseline multi-feature learning method.
Identifying drug-target interactions (DTIs) is a major challenge in drug development. Traditionally, similarity-based methods use drug and target similarity matrices to infer the potential drug-target interactions. Bu...
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ISBN:
(纸本)9781509006199
Identifying drug-target interactions (DTIs) is a major challenge in drug development. Traditionally, similarity-based methods use drug and target similarity matrices to infer the potential drug-target interactions. But these techniques do not handle biochemical data directly. While recent feature-based methods reveal simple patterns of physicochemical properties, efficient method to study large interactive features and precisely predict interactions is still missing. Deep learning has been found to be an appropriate tool for converting high-dimensional features to low-dimensional representations. These deep representations generated from drug-protein pair can serve as training examples for the interaction predictor. In this paper, we propose a promising approach called multi-scale features deep representations inferring interactions (MFDR). We extract the large-scale chemical structure and protein sequence descriptors so as to machine learning model predict if certain human target protein can interact with a specific drug. MFDR use Auto-Encoders as building blocks of deep network for reconstruct drug and protein features to low-dimensional new representations. Then, we make use of support vector machine to infer the potential drug-target interaction from deep representations. The experiment result shows that a deep neural network with Stacked Auto-Encoders exactly output interactive representations for the DTIs prediction task. MFDR is able to predict large-scale drug-target interactions with high accuracy and achieves results better than other feature-based approaches.
In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling ...
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ISBN:
(纸本)9781509009817
In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling problem, i.e., each pixel is assigned to one of a set of labels. Features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. The features are used for the purpose of initial classification with a multilayer perceptron. Then a CRF model is introduced for modeling the local and contextual information jointly in order to improve the segmentation. For the purpose of decreasing the time complexity, we perform labeling at superpixel level. In the CRF model, graph nodes are represented by superpixels. The label of each pixel is determined by the label of the superpixel to which it belongs. Experiments on three public datasets demonstrate that, compared to previous methods, the proposed method achieves more accurate segmentation results and is much faster.
Fraud detection in electricity consumption is a major challenge for power distribution companies. While many pattern recognition techniques have been applied to identify electricity theft, they often require extensive...
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ISBN:
(纸本)9781509061679
Fraud detection in electricity consumption is a major challenge for power distribution companies. While many pattern recognition techniques have been applied to identify electricity theft, they often require extensive handcrafted feature engineering. Instead, through deep layers of transformation, nonlinearity, and abstraction, Deep Learning (DL) automatically extracts key features from data. In this paper, we design spatial and temporal deep learning solutions to identify nontechnical power losses (NTL), including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Stacked autoencoder. These models are evaluated in a modified IEEE 123-bus test feeder. For the same tests, we also conduct comparison experiments using three conventional machine learning approaches: Random Forest, Decision Trees and shallow Neural Networks. Experimental results demonstrate that the spatiotemporal deep learning approaches outperform conventional machine learning approaches.
The field of machine learning deals with a huge amount of various algorithms, which are able to transform the observed data into many forms and dimensionality reduction (DR) is one of such transformations. There are m...
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The field of machine learning deals with a huge amount of various algorithms, which are able to transform the observed data into many forms and dimensionality reduction (DR) is one of such transformations. There are many high quality papers which compares some of the DR's approaches and of course there other experiments which applies them with success. Not everyone is focused on information lost, increase of relevance or decrease of uncertainty during the transformation, which is hard to estimate and only few studies remark it briefly. This study aims to explain these inner features of four different DR's algorithms. These algorithms were not chosen randomly, but in purpose. It is chosen some representative from all of the major DR's groups. The comparison criteria are based on statistical dependencies, such as Correlation Coefficient, Euclidean Distance, Mutual Information and Granger causality. The winning algorithm should reasonably transform the input dataset with keeping the most of the inner dependencies. (C) 2016 The Authors. Published by Elsevier B.V.
We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, ...
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ISBN:
(纸本)9781509006199
We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a paritition function. This paper also formalizes, generative moment matching networks under the ITL framework.
In the recent years, the vast volume of digital images available enabled a large range of learning methods to be applicable, while making human input obsolete for many tasks. In this paper, we are addressing the probl...
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ISBN:
(纸本)9781509057078
In the recent years, the vast volume of digital images available enabled a large range of learning methods to be applicable, while making human input obsolete for many tasks. In this paper, we are addressing the problem of removing private information from images. When confronted with a relatively big number of pictures to be made public, one may find the task of manual editing out sensitive regions to be unfeasible. Ideally, we would like to use a machine learning approach to automate this task. We implement and compare different architectures based on convolutional neural networks, with generative and discriminative models competing in an adversarial fashion.
To elongate the battery life of sensors worn in wireless body area networks, recent studies have advocated compressing the acquired biological signals before transmitting them. The signals are compressed using compres...
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ISBN:
(纸本)9781509006199
To elongate the battery life of sensors worn in wireless body area networks, recent studies have advocated compressing the acquired biological signals before transmitting them. The signals are compressed using compressive sensing (CS), by projecting them onto a lower dimension. The original signals are then recovered using CS recovery techniques at the base station, where the computational power is assumed to be abundant. This assumption however is not entirely true when a mobile phone acts as the base station. The computational capacity of a mobile phone is limited;therefore solving the CS recovery problem in the phone would be time consuming. In many cases (e,g. heart stroke detection or monitoring applications) this latency cannot be tolerated. In this work we propose a new technique to solve the inverse problem using stacked autoencoders. We show that the reconstruction of the proposed method can be done in real-time, and there is only a slight degradation in accuracy compared to CS based inversion methods.
This paper provides an efficient framework for recognizing human interactions based on deep learning based architecture. The Harris corner points and the histogram form the feature vector of the spatiotemporal volume....
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ISBN:
(纸本)9781509010721
This paper provides an efficient framework for recognizing human interactions based on deep learning based architecture. The Harris corner points and the histogram form the feature vector of the spatiotemporal volume. The feature vector extraction is restricted to the region of interaction. A stacked autoencoder configuration is embedded in the deep learning framework used for classification. The method is evaluated on the benchmark UT interaction dataset and average recognition rates as high as 95% and 88% are obtained on set1 and set2 respectively.
Using content-based binary codes to tag digital images has emerged as a promising retrieval technology. Recently, Radon barcodes (RBCs) have been introduced as a new binary descriptor for image search. RBCs are genera...
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ISBN:
(纸本)9781509048472
Using content-based binary codes to tag digital images has emerged as a promising retrieval technology. Recently, Radon barcodes (RBCs) have been introduced as a new binary descriptor for image search. RBCs are generated by binarization of Radon projections and by assembling them into a vector, namely the barcode. A simple local thresholding has been suggested for binarization. In this paper, we put forward the idea of "autoencoded Radon barcodes". Using images in a training dataset, we autoencode Radon projections to perform binarization on outputs of hidden layers. We employed the mini-batch stochastic gradient descent approach for the training. Each hidden layer of the autoencoder can produce a barcode using a threshold determined based on the range of the logistic function used. The compressing capability of autoencoders apparently reduces the redundancies inherent in Radon projections leading to more accurate retrieval results. The IRMA dataset with 14,410 x-ray images is used to validate the performance of the proposed method. The experimental results, containing comparison with RBCs, SURF and BRISK, show that autoencoded Radon barcode (ARBC) has the capacity to capture important information and to learn richer representations resulting in lower retrieval errors for image retrieval measured with the accuracy of the first hit only.
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