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Semi-supervised dimensional sentiment analysis with variational autoencoder

KNOWLEDGE-BASED SYSTEMS 2019年 165卷 30-39页

作者： Wu, Chuhan Wu, Fangzhao Wu, Sixing Yuan, Zhigang Liu, Junxin Huang, Yongfeng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Microsoft Res Asia Beijing 100084 Peoples R China

Dimensional sentiment analysis (DSA) aims to compute real-valued sentiment scores of texts in multiple dimensions such as valence and arousal. Existing methods for DSA are usually based on supervised learning. However, it is expensive and time-consuming to annotate sufficient samples for training. In this paper, we propose a semi-supervised approach for DSA based on the variational autoencoder model. Our model consists of three modules: an encoding module to encode sentences into hidden vectors, a sentiment prediction module to predict the sentiment scores of sentences, and a decoding module that takes the outputs of the preceding two modules as input and reconstructs the input sentences. In our approach, the sentiment prediction module is encouraged to accurately predict sentiment scores of both labeled and unlabeled texts to help the decoding module reconstruct such texts more accurately. Thus, our approach can exploit useful information in unlabeled data. Experimental results on three benchmark datasets show that our approach can effectively improve the performance of DSA with considerably less labeled data. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Dimensional sentiment analysis variational autoencoder Semi-supervised learning

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Collaborative Additional variational autoencoder for Top-N Recommender Systems

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IEEE ACCESS 2019年 7卷 5707-5713页

作者： He, Ming Meng, Qian Zhang, Shaozong Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance. Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems. However, they seem to fail to take into consideration the fact that the sparsity of single side information. To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item. The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner. With the help of graphical models, it can extract the implicit relationships between users and items effectively. A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved.

关键词： Collaborative filtering deep learning variational autoencoder side information

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Process monitoring using variational autoencoder for high-dimensional nonlinear processes

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 83卷 13-27页

作者： Lee, Seulki Kwak, Mingu Tsui, Kwok-Leung Kim, Seoung Bum Korea Univ Sch Ind Management Engn 145 Anam Ro Seoul 02841 South Korea City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong 999077 Peoples R China

In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling's T-2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T-2 charts because it causes the reduced space to follow a multivariate normal distribution. The effectiveness and applicability of the proposed VAE-based chart were demonstrated through experiments on simulated data and real data from a thin-film-transistor liquid-crystal display process.

关键词： Statistical process monitoring variational autoencoder High-dimensional process Nonlinear process Multivariate control chart

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EEG-Based Adaptive Driver-Vehicle Interface Using variational autoencoder and PI-TSVM

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2019年第10期27卷 2025-2033页

作者： Bi, Luzheng Zhang, Jingwei Lian, Jinling Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Event-related potential (ERP)-based driver-vehicle interfaces (DVIs) have been developed to provide a communication channel for people with disabilities to drive a vehicle. However, they require a tedious and time-consuming training procedure to build the decoding model, which can translate EEG signals into commands. In this paper, to address this problem, we propose an adaptive DVI by using a new semi-supervised algorithm. The decoding model of the proposed DVI is first built with a small labeled training set, and then gradually improved by updating the proposed semi-supervised decoding model with new collected unlabeled EEG signals. In our semi-supervised algorithm, independent component analysis (ICA) and Kalman smoother are first used to improve the signal-to-noise ratio (SNR). After that, variational autoencoder is applied to provide a robust feature representation of EEG signals. Finally, a prior information-based transductive support vector machine (PI-TSVM) classifier is developed to translate these features into commands. Experimental results show that the proposed DVI can significantly reduce the training effort. After a short updating, its performance can be close to that of the supervised DVI requiring a lengthy training procedure. This work is vital for advancing the application of these DVIs.

关键词： Brain-controlled vehicle EEG semi-supervised learning variational autoencoder transductive support vector machine

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Semi-supervised Aspect-level Sentiment Classification Model based on variational autoencoder

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KNOWLEDGE-BASED SYSTEMS 2019年第May 1期171卷 81-92页

作者： Fu, Xianghua Wei, Yanzhi Xu, Fan Wang, Ting Lu, Yu Li, Jianqiang Huang, Joshua Zhexue Shenzhen Technol Univ Fac Arts & Sci Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Technol Univ Coll Creat Design Shenzhen Peoples R China

Aspect-level sentiment classification aims to predict the sentiment of a text in different aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention-based Long Short-Term Memory Network to perform aspect-level sentiment classification. Most previous work are based on supervised learning that needs a large number of labeled samples, but the problem is that only a limited subset of data samples are labeled in practical applications. To solve this problem, we propose a novel Semi-supervised Aspect Level Sentiment Classification Model based on variational autoencoder (AL-SSVAE) for semi-supervised learning in the aspect-level sentiment classification. The AL-SSVAE model inputs a given aspect to an encoder a decoder based on a variational autoencoder (VAE), and it also has an aspect level sentiment classifier. It enables the attention mechanism to deal with different parts of a text when different aspects are taken as input as previous methods. Due to that the sentiment polarity of a word is usually sensitive to the given aspect, a single vector for a word is problematic. Therefore, we propose the aspect-specific word embedding learning from a topical word embeddings model to express a word and also append the corresponding sentiment vector into the word input vector. We compare our AL-SSVAE model with several recent aspect-level sentiment classification models on the SemEval 2016 dataset. The experimental results indicate that the proposed model is able to capture more accurate semantics and sentiment for the given aspect and obtain better performance on the task of the aspect level sentiment classification. Moreover, the AL-SSVAE model is able to learn with the semi-supervised mode in the aspect level sentiment classification, which enables it to learn efficiently using less labeled data. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Aspect-level variational autoencoder Word embeddings Semi-supervised Sentiment classification

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Speech Source Separation Using variational autoencoder and Bandpass Filter

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IEEE ACCESS 2020年 8卷 156219-156231页

作者： Do, Hao Duc Tran, Son Thai Chau, Duc Thanh Univ Sci Fac Informat Technol Ho Chi Minh City 70000 Vietnam Viet Nam Natl Univ Ho Chi Minh City 70000 Vietnam Univ Sci Off Educ & Training Ho Chi Minh City 70000 Vietnam OLLI Technol JSC AI Lab Ho Chi Minh City 70000 Vietnam

Speech source separation is essential for speech-related applications because this process enhances the input speech signal for the main processing model. Most of the current approaches for this task focus on separating the speech of commonly high-frequency noises or a particular background sound. They cannot clear the signals which intersect with the human speech in its frequency range. To deal with this problem, we propose a hybrid approach combining a variational autoencoder (VAE) and a bandpass filter (BPF). This method can extract and enhance the speech signal in the mixture of many elements such as speech signal, the high-frequency noises, and many kinds of different background sounds which interfere with the speech sound. Experimental results showed that our model can extract effectively the speech signal with 15.02 dB in Signal to Interference Ratio (SIR) and 12.99 dB in Signal to Distortion Ratio (SDR). On the other hand, we can adjust the passband to identify the range of frequency at the output signal to apply for a particular application like gender classification.

关键词： Speech enhancement Blind source separation Band-pass filters Music Multiple signal classification Generative model variational autoencoder bandpass filter speech separation

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A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

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BMC BIOINFORMATICS 2020年第1期21卷 64-64页

作者： Lin, Eugene Mukherjee, Sudipto Kannan, Sreeram Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Univ Washington Dept Biostat Seattle WA 98195 USA China Med Univ Grad Inst Biomed Sci Taichung Taiwan

Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). Results To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. Conclusions Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

关键词： Adversarial autoencoder variational autoencoder Dimensionality reduction Generative adversarial networks Single-cell RNA sequencing

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Dirichlet variational autoencoder

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PATTERN RECOGNITION 2020年 107卷 107514-107514页

作者： Joo, Weonyoung Lee, Wonsung Park, Sungrae Moon, Il-Chul Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea SK Telecom AI Technol Unit Seoul 04539 South Korea NAVER Corp Clova AI Res Gyeonggi Do 13561 South Korea

This paper proposes Dirichlet variational autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution. This approximation on a new prior led an investigation on the component collapsing, and DirVAE revealed that the component collapsing originates from two problem sources: decoder weight collapsing and latent value collapsing. The experimental results show that 1) DirVAE generates the result with the best log-likelihood compared to the baselines;2) DirVAE produces more interpretable latent values with no collapsing issues which the baselines suffer from;3) the latent representation from DirVAE achieves the best classification accuracy in the (semi-)supervised classification tasks on MNIST, OMNIGLOT, COIL-20, SVHN, and CIFAR-10 compared to the baseline VAEs;and 4) the DirVAE augmented topic models show better performances in most cases. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Representation learning variational autoencoder Deep generative model Multi-modal latent representation Component collapse

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A mutual information-based variational autoencoder for robust JIT soft sensing with abnormal observations

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 204卷 104118-104118页

作者： Guo, Fan Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

Considering industrial process with high-dimensional, intrinsic nonlinearities and possibly abnormal observations, a robust deep learning soft sensor model is developed under the just-in-time learning framework. As an unsupervised deep learning approach, variational autoencoder (VAE) has been successfully applied to soft sensing problems owing to its ability to describe the latent representations by probability distributions. In this work, to construct high performance soft sensor model, mutual information (MI) is first introduced for input variable selection. By further incorporating MI as weights on variable of the traditional VAE model, a MI-based output-relevant VAE is developed. For each new sample that arrives, by utilizing Symmetric Kullback-Leibler (SKL) divergence, its relevance with historical samples is determined. Based on the SKL divergence, the input samples that are most relevant to the query sample can be collected. The selected historical input samples and corresponding output samples are employed to build a Gaussian process regression (GPR) local model. Expectation maximation (EM) algorithm is utilized to deal with the nonlinearity and abnormal output observations in GPR local model simultaneously for robustness of the soft sensors. Numerical simulations and a benchmark process are employed to validate the effectiveness of the proposed soft sensor, which demonstrates its superior performance over traditional approaches.

关键词： variational autoencoder Mutual information Just-in-time learning Outliers Missing data

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Semi-deterministic and Contrastive variational Graph autoencoder for Recommendation 21

Semi-deterministic and Contrastive Variational Graph Autoenc...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ding, Yue Shi, Yuxiang Chen, Bo Lin, Chenghua Lu, Hongtao Li, Jie Tang, Ruiming Wang, Dong Shanghai Jiao Tong Univ Shanghai Peoples R China Noahs Ark Lab Huawei Montreal PQ Canada Univ Sheffield Sheffield S Yorkshire England

ISBN: (纸本)9781450384469

variational autoencoder (VAE) is a popular deep generative framework with a solid theoretical basis. There are many research efforts on improving VAE. Among the existing works, a recently proposed deterministic Regularized autoencoder (RAE) provides a new scheme for generative modeling. RAE fixes the variance of the inferred Gaussian approximate posterior distribution as a hyperparameter, and substitutes the stochastic encoder by injecting noise into the input of a deterministic decoder. However, the deterministic RAE has three limitations: 1) RAE needs to fit the variance;2) RAE requires ex-post density estimation to ensure sample quality;3) RAE employs an additional gradient regularization to ensure training smoothness. Thus, it raises an interesting research question: Can we maintain the flexibility of variational inference while simplifying VAE, and at the same time ensuring a smooth training process to obtain good generative performance? Based on the above motivation, in this paper, we propose a novel Semi-deterministic and Contrastive variational Graph autoencoder (SCVG) for item recommendation. The core design of SCVG is to learn the variance of the approximate Gaussian posterior distribution in a semi-deterministic manner by aggregating inferred mean vectors from other connected nodes via graph convolution operation. We analyze the expressive power of SCVG for the Weisfeiler-Lehman graph isomorphism test, and we deduce the simplified form of the evidence lower bound of SCVG. Besides, we introduce an efficient contrastive regularization instead of gradient regularization. We empirically show that the contrastive regularization makes learned user/item latent representation more personalized and helps to smooth the training process. We conduct extensive experiments on three real-world datasets to show the superiority of our model over state-of-the-art methods for the item recommendation task. Codes are available at https://***/syxkason/SCVG.

关键词： variational autoencoder Graph Neural Network Mutual Information Maximization Collaborative Filtering

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