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Self-adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy

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IET COMPUTER VISION 2020年第3期14卷 73-83页

作者： Du, Lingshuang Wu, Yongbo Hu, Haifeng Wang, Weixuan Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China

This study presents a novel method for solving facial expression recognition (FER) tasks which uses a self-adaptive weighted synthesised local directional pattern (SW-SLDP) descriptor integrating sparse autoencoder (SA) features based on improved multiple kernel learning (IMKL) strategy. The authors' work includes three parts. Firstly, the authors propose a novel SW-SLDP feature descriptor which divides the facial images into patches and extracts sub-block features synthetically according to both distribution information and directional intensity contrast. Then self-adaptive weights are assigned to each sub-block feature according to the projection error between the expressional image and neutral image of each patch, which can highlight such areas containing more expressional texture information. Secondly, to extract a discriminative high-level feature, they introduce SA for feature representation, which extracts the hidden layer representation including more comprehensive information. Finally, to combine the above two kinds of features, an IMKL strategy is developed by effectively integrating both soft margin learning and intrinsic local constraints, which is robust to noisy condition and thus improve the classification performance. Extensive experimental results indicate their model can achieve competitive or even better performance with existing representative FER methods.

关键词： image representation feature extraction image classification image texture face recognition learning (artificial intelligence) self-adaptive weighted synthesised local directional pattern sparse autoencoder improved multiple kernel learning strategy facial expression recognition tasks novel SW-SLDP feature descriptor facial images extracts sub-block features directional intensity contrast self-adaptive weights sub-block feature expressional image expressional texture information discriminative high-level feature feature representation IMKL strategy high-level features

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sparse autoencoder for social image understanding

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NEUROCOMPUTING 2019年 369卷 122-133页

作者： Liu, Jianran Wang, Shiping Yang, Wenyuan Minnan Normal Univ Fujian Key Lab Granular Comp & Applicat Zhangzhou 363000 Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350000 Fujian Peoples R China

The rapid increase of social media images has made organizing these resources effectively a huge problem. Labeling unlabeled images becomes the crucial division of social image understanding. However, the enhancement of social image sharpness leads to the increase of surface feature dimension. These multidimensional complex features leads to the curse of dimensionality and the difficulty of feature extraction. In this paper, sparse autoencoder is studied to solve the problem of social image understanding, because sparse autoencoder can make these features represent the original data in a refined way, thus avoiding curse of dimensionality as much as possible and significantly improve the understanding effect. First, we explore the dimensional reduction capability of sparse autoencoder, and use sparse autoencoder to get low-dimensional features. Second, for low-dimensional features, an enhanced multi-label classifier is utilized to assign labels with the help of cosine similarity about tags correlation. The ability of dimensionality reduction of sparse autoencoder is proved by mapping matrix of image-label. Finally, we test our approach on several publicly available social media datasets. The results demonstrate that our proposed method is superior to lots of non-deep learning method among three evaluation indexes of social image understanding. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Machine learning Dimensionality reduction Image understanding sparse autoencoder Multi-label predict

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ASD-SAENet: A sparse autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 654315页

作者： Almuqhim, Fahad Saeed, Fahad Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://***/pcdslab/ASD- SAENet.

关键词： ASD fMRI autoencoder sparse autoencoder ABIDE deep-learning classification diagnosis

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Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷 106948-106948页

作者： Li, Yakun Ren, Jiadong Liu, Jiaomin Chang, Yixin Yanshan Univ Coll Informat Sci & Engn Qinhuangdao Hebei Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao Hebei Peoples R China

Online recommender systems generally suffer from severe data sparsity problems, and this are particularly prevalent in newly launched systems that do not have sufficient amounts of data. Cross-domain recommendations can provide us with some new ideas for assisting with user recommendations in sparse target domains by transferring knowledge from a source domain with rich data. In this paper, a deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations (DSAP-AL) is proposed to improve the accuracy of rating predictions in similar cross domain recommender systems. Specifically, joint matrix factorization and adversarial network learning models are adopted to integrate and align user and item latent factor spaces in a unified pattern. Then, a deep sparse autoencoder is represented and modeled by transferring the latent factors and interlayer weights. Furthermore, a domain factor adaptation algorithm is proposed to capture robust user and item factors, and the learned regularization constraints are added to the objective function, thereby alleviating the data sparsity issue. Experimental results on four real-world datasets demonstrate that, even without overlapping entities (users or items) in the source and target domains, the proposed DSAP-AL method achieves competitive performance relative to other state-of-the-art individual and cross domain approaches. Moreover, the DSAP-AL model is not only effective for scenarios with sparse data but also robust for noise-containing recommendations. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Data sparsity sparse autoencoder Adversarial learning Recommender systems Matrix factorization

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Fault Diagnosis Based on Batch-normalized Stacked sparse autoencoder 39

Fault Diagnosis Based on Batch-normalized Stacked Sparse Aut...

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39th Chinese Control Conference (CCC)

作者： Liu Xiaozhi Gao Yang Yang Yinghua Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

A fault diagnosis method based on batch-normalization stacked sparse autoencoder (SSAE) is presented in this paper. This paper use the autoencoder to extract features for fault diagnosis on account of its good performance in feature extraction. In order to improve the accuracy of the extracted features, this paper use a sparse representation which is a constraint during the encoding process. The multi-layer structure of autoencoder has an internal covariate shift problem and the generalization ability of the network is critical, batch normalization is employed before the activation function in each layer of the autoencoder network. And a stacked method is utilized to optimize network structure and reduce training difficulty. So the features of the original signal are extracted by the network based on the above method and the extracted features are placed in the classifier to identify different health states. For the purpose of fault diagnosis, this paper uses the proposed method to experiment with the bearing data set provided by Case Western Reserve University (CWRU). The experiment proves that the proposed method has a better fault diagnosis performance compared with other traditional methods.

关键词： Fault diagnosis sparse autoencoder Batch-normalization

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sparse autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

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IEEE ACCESS 2020年 8卷 153250-153263页

作者： Chen, Xiuyuan Peng, Xiyuan Li, Jun-Bao Huo, Chaoying Harbin Inst Technol Harbin 150080 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China Sci & Technol Electromagnet Scattering Lab Beijing 100039 Peoples R China

Automatic target recognition (ATR) has always been an important research topic, and the performance is affected by feature extraction. High-resolution range profiles (HRRP) contains structural information of target from different angles. Existing target recognition algorithms are mostly adopt the supervised learning and verify the validity through classification accuracy. However, these methods cannot satisfy the information acquisition such as correlation, composition analysis and attitude analysis of unlabeled or uncoordinated targets. Therefore, we are committed to researching new intelligent feature extraction and analysis method. In this article, a novel learning framework is proposed to realize the angle correlation feature extraction and feature analysis of multi-angle HRRP targets. A multi-layer sparse autoencoder is applied to extract HRRP features and the extracted features are mapped to the low-dimensional space by manifold learning method to find the correlation distribution of HRRP data. The effectiveness of the proposed framework is demonstrated by experiments with simulated and measured data. The experimental results show that the framework realizes the extraction of angle-invariant features, and the analysis of distribution relationship between HRRP data and angles. The research results of the correspondence between the assembly and the related geometry provide the basis and possibility for further identification and composition determination.

关键词： ATR feature extraction HRRP manifold analyzer sparse autoencoder

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sparse autoencoder with Attention Mechanism for Speech Emotion Recognition 1

Sparse Autoencoder with Attention Mechanism for Speech Emoti...

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1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Sun, Ting-Wei Wu, An-Yeu (Andy) Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan

ISBN: (纸本)9781538678848

There has been a lot of previous works on speech emotion with machine learning method. However, most of them rely on the effectiveness of labelled speech data. In this paper, we propose a novel algorithm which combines both sparse autoencoder and attention mechanism. The aim is to benefit from labeled and unlabeled data with autoencoder, and to apply attention mechanism to focus on speech frames which have strong emotional information. We can also ignore other speech frames which do not carry emotional content. The proposed algorithm is evaluated on three public databases with cross-language system. Experimental results show that the proposed algorithm provide significantly higher accurate predictions compare to existing speech emotion recognition algorithms.

关键词： Affective computing Speech Emotion sparse autoencoder Attention Mechanism

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EndNet: sparse autoencoder Network for Endmember Extraction and Hyperspectral Unmixing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第1期57卷 482-496页

作者： Ozkan, Savas Kaya, Berk Akar, Gozde Bozdagi TUBITAK Space Technol Res Inst Image Proc Dept TR-06800 Ankara Turkey Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors, and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so-called EndNet, that is based on a two-staged autoencoder network. This well-known structure is completely enhanced and restructured by introducing additional layers and a projection metric [i.e., spectral angle distance (SAD) instead of inner product] to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers, such as nonlinearity and sparsity for autoencoder networks. Finally, due to the stochastic-gradient-based approach, the method is scalable for large-scale data and it can be accelerated on graphical processing units. To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known data sets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in the literature.

关键词： Endmember extraction hyperspectral unmixing sparse autoencoder

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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion

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EVOLUTIONARY INTELLIGENCE 2021年第1期14卷 133-149页

作者： Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam Maulana Abul Kalam Azad Univ Technol Kolkata 700068 India Univ Calcutta Kolkata 700098 India Netaji Subhash Engn Coll Kolkata 700152 India

This paper proposes a deep unsupervised learning based denoising autoencoder model for the restoration of degraded mammogram with visual interpretation of breast lumps or lesion in mammography images (called SSDAE). The proposed model attempts to intensify the underexposed and abnormal structural regions through noise elimination in mammography image. A deep stacked convolutional autoencoder is designed by combining the autoencoder and the deconvolution network which conjointly reduces noisy artifacts and improves image details in mammogram. The proposed SSDAE model takes large noisy mammogram image patches as input and extracts relevant features from target batches. The suggested model can extract relevant features and reduce the dimensionality through sparsity property of the image data while preserving the key features that have been applied to restore image data in feature space. In order to reconstruct a deafening mammogram, the proposed model is carried out through a patched base training on samples to suppress noise thereby preserving structural details in mammography imaging. Experimental results authenticate that the suggested SSDAE model outplays a number of state-of-the-art methods for both X-ray mammogram and ultrasonographic mammogram. The execution speed for target noisy images increases with fine tuning of the network when compared to other algorithms.

关键词： Deep learning Mammogram restoration Convolutional autoencoder sparse autoencoder KL divergence

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Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier

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MATHEMATICAL BIOSCIENCES 2019年 311卷 103-108页

作者： Yang Qing Jia Cangzhi Li Taoying Dalian Maritime Univ Inst Environm Syst Biol Coll Environm & Engn 1 Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Sch Sci 1 Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Dept Maritime Econ & Management 1 Linghai Rd Dalian 116026 Peoples R China

Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub-support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.

关键词： Amino acid composition Machine learning sparse autoencoder Ensemble learning Feature selection

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