The original hyperspectral data served as the initial features has the characteristics of high dimension and redundancy, which is not suitable for the subsequent analysis, so extracting feature information is needed. ...
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The original hyperspectral data served as the initial features has the characteristics of high dimension and redundancy, which is not suitable for the subsequent analysis, so extracting feature information is needed. The deep learning model has a strong ability in feature learning, but if the model has too many layers which will lead to the original information loss in the process of layer-by-layer feature learning and reduce the subsequent classification accuracy. To solve this problem, the paper proposed a deep learning model of hybrid structure with the contractive autoencoder and restricted boltzmann machine to extract the hyperspectral data feature information. First, through pre-processing the spectral data, the 2d spectrum data is converted into a one dimensional vector. Then, a hybrid model is constructed for unsupervised training and supervised learning for the hyperspectral data, and features are extracted from bottom to top gradually according to the hybrid model. Finally, the SVM classifier is adopted to enhance the classification ability of spectral data. The paper uses the hybrid model proposed to test for extracting features with two sets of AVIRIS data and compares with PCA and GCA methods. The experiment results show that the feature extraction algorithm based on hybrid depth model can get the better features, and have strong distinguish performance, and can get better classification accuracy by the SVM algorithm.
Harmonic mapping provides a natural way of mapping two manifolds by minimizing distortion induced by the mapping. However, most applications are limited to mapping between 2D and/or 3D spaces owing to the high computa...
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Harmonic mapping provides a natural way of mapping two manifolds by minimizing distortion induced by the mapping. However, most applications are limited to mapping between 2D and/or 3D spaces owing to the high computational cost. We propose a novel approach, the harmonic autoencoder (HAE), by approximating a harmonic mapping in a data-driven way. The HAE learns a mapping from an input domain to a target domain that minimizes distortion and requires only a small number of input-target reference pairs. The HAE can be applied to high-dimensional applications, such as human-to-robot hand pose mapping. Our method can map from the input to the target domain while minimizing distortion over the input samples, covering the target domain, and satisfying the reference pairs. This is achieved by extending an existing neural network method called the contractive autoencoder. Starting from a contractive autoencoder, the HAE takes into account a distance function between point clouds within the input and target domains, in addition to a penalty for estimation error on reference points. For efficiently selecting a set of input-target reference pairs during the training process, we introduce an adaptive optimization criterion. We demonstrate that pairs selected in this way yield a higher-performance mapping than pairs selected randomly, and the mapping is comparable to that from pairs selected heuristically by the experimenter. Our experimental results with synthetic data and human-to-robot hand pose data demonstrate that our method can learn an effective mapping between the input and target domains.
Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data. The significant challenges in effectively detecting fraud...
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Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data. The significant challenges in effectively detecting fraud stem from the inherent class imbalance between normal and fraudulent instances. To address this issue, we propose a novel approach that combines autoencoder-based noise factor encoding (NFE) with the synthetic minority oversampling technique (SMOTE). Our study evaluates the efficacy of this approach using three datasets with severe class imbalance. We compare three autoencoder variants-autoencoder (AE), variational autoencoder (VAE), and contractive autoencoder (CAE)-enhanced by the NFE technique. This technique involves training autoencoder models on real fraud data with an added noise factor during the encoding process, followed by combining this altered data with genuine fraud data. Subsequently, SMOTE is employed for oversampling. Through extensive experimentation, we assess various evaluation metrics. Our results demonstrate the superiority of the autoencoder-based NFE approach over the use of traditional oversampling methods like SMOTE alone. Specifically, the AE-NFE method outperforms other techniques in most cases, although the VAE-NFE and CAE-NFE methods also exhibit promising results in specific scenarios. This study highlights the effectiveness of leveraging autoencoder-based NFE and SMOTE for fraud detection. By addressing class imbalance and enhancing the performance of fraud detection models, our approach enables more accurate identification and prevention of fraudulent activities in real-world applications.
Single-cell sequencing technologies are widely used to discover the evolutionary relationships and the differences in cells. Since dropout events may frustrate the analysis, many imputation approaches for single-cell ...
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Single-cell sequencing technologies are widely used to discover the evolutionary relationships and the differences in cells. Since dropout events may frustrate the analysis, many imputation approaches for single-cell RNA-seq data have appeared in previous attempts. However, previous imputation attempts usually suffer from the over-smooth problem, which may bring limited improvement or negative effect for the downstream analysis of single-cell RNA-seq data. To solve this difficulty, we propose a novel two-stage diffusion-denoising method called SCDD for large-scale single-cell RNA-seq imputation in this paper. We introduce the diffusion i.e. a direct imputation strategy using the expression of similar cells for potential dropout sites, to perform the initial imputation at first. After the diffusion, a joint model integrated with graph convolutional neural network and contractive autoencoder is developed to generate superposition states of similar cells, from which we restore the original states and remove the noise introduced by the diffusion. The final experimental results indicate that SCDD could effectively suppress the over-smooth problem and remarkably improve the effect of single-cell RNA-seq downstream analysis, including clustering and trajectory analysis.
It is hard to avoid recommender systems (RS) these days which play a vital role in various domains, such as e-commerce, online streaming platforms, and personalized content delivery. These systems assist users in disc...
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ISBN:
(纸本)9798400701245
It is hard to avoid recommender systems (RS) these days which play a vital role in various domains, such as e-commerce, online streaming platforms, and personalized content delivery. These systems assist users in discovering relevant items based on their preferences and past interactions. A variety of methods are developed by RS communities, to address the accuracy issue. But, most of these methods are sequential and omit the item features and user side information which contains relevant and rich information that can increase the accuracy of these systems. However, enhancing the accuracy of recommendations often comes at the expense of increased computational costs. This PhD thesis aims to address the challenge of improving the accuracy of RS following a hybrid approach that allows leveraging user-item features based on some relevant state-of-the-art models and using deep learning techniques such as the contractive autoencoder (CAE) while optimizing the cost of computation using parallel/distributed paradigms.
Collaborative Filtering (CF) has been a highly effective approach widely utilized in Recommender Systems, leveraging user-item interactions. Among CF methods, Matrix Factorization (MF) has maintained a prominent statu...
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ISBN:
(纸本)9783031770425;9783031770432
Collaborative Filtering (CF) has been a highly effective approach widely utilized in Recommender Systems, leveraging user-item interactions. Among CF methods, Matrix Factorization (MF) has maintained a prominent status as a state-of-the-art model, delivering personalized item recommendations based on user preferences. Recent advancements have integrated Deep Learning techniques into MF, particularly to capture nonlinear item representations and enhance recommendation accuracy. However, many of these models often overlook user-item features and fail to offer understandable explanations for their recommendations. This absence of explanation mechanisms undermines user trust in recommendations, reducing their relevance and utility. In this study, we introduce E-IUAutoMF, an extension of MF that incorporates explainable user-item based features. This model builds upon the Explainable Matrix Factorization (EMF) framework, utilizing contractive autoencoders to extract user and item features and providing explanations using neighborhood explanation techniques. The results of several experiments demonstrate that our proposed model E-IUAutoMF outperforms the other baselines models.
Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a sli...
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Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely tradi
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