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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hua-Hao Liu Pei-Jie Huang Pi-Yuan Lin Wen-Hu Lin Pei-Heng Qi Chong-Hua Song College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (纸本)9781467372220

Feature representation is one of the key research issues in machine learning. In some applications with high dimensionality of data, e.g. genomie microarray data, obtaining a good feature representation with effective dimensionality reduction still remains a challenge. In this paper, instead of selecting a subset of original features by feature selection, we use sparse autoencoder to find a reconstructed feature representation for genetic data analysis. The performance of our proposed method is empirically evaluated using one of the genomie microarray dataset provided in ASU Feature Selection Repository. The results show that the proposed method yield better classification accuracy than some representative feature selection methods.

关键词： Feature representation Genetic data analysis Machine learning sparse autoencoder Machine Learning Artificial Intelligence Feature extraction genetic information Data analysis dimensionality reduction Microarray

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Semi-Supervised autoencoder : A Joint Approach of Representation And Classification 7

Semi-Supervised Autoencoder : A Joint Approach of Representa...

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Wu Haiyan Yang Haomin Li Xueming Ren Haijun Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781509000760

Recent years have witnessed the significant success of representation learning and deep learning in various prediction and recognition applications. Most of these previous studies adopt the two-phase procedures, namely the first step of representation learning and then the second step of supervised learning. In this process, to fit the training data the initial model weights, which inherits the good properties from the representation learning in the first step, will be changed in the second step. In other words, the second step leans better classification models at the cost of the possible deterioration of the effectiveness of representation learning. Motivated by this observation we propose a joint framework of representation and supervised learning. It aims to learn a model, which not only guarantees the "semantics" of the original data from representation learning but also fit the training data well via supervised learning. Along this line we develop the model of semi-supervised autoencoder under the spirit of the joint learning framework. The experiments on various data sets for classification show the significant effectiveness of the proposed model.

关键词： semi-supervised autoencoder deep learning logistic regression sparse autoencoder

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No-reference image quality assessment using statistical characterization in the shearlet domain

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2014年第7期29卷 748-759页

作者： Li, Yuming Po, Lai-Man Xu, Xuyuan Feng, Litong City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Image and video quality measurements are crucial for many applications, such as acquisition, compression, transmission, enhancement, and reproduction. Nowadays, no-reference (NR) image quality assessment (IQA) methods have drawn extensive attention because it does not rely on any information of original images. However, most of the conventional NR-IQA methods are designed only for one or a set of predefined specific image distortion types, which are unlikely to generalize for evaluating image/video distorted with other types of distortions. In order to estimate a wide range of image distortions, in this paper, we present an efficient general-purpose NR-IQA algorithm which is based on a new multiscale directional transform (shearlet transform) with a strong ability to localize distributed discontinuities. This is mainly based on distorted natural image that leads to significant variation in the spread discontinuities in all directions. Thus, the statistical property of the distorted image is significantly different from that of natural images in fine scale shearlet coefficients, which are referred to as 'distorted parts'. However, some 'natural parts' are reserved in coarse scale shearlet coefficients. The algorithm relies on utilizing the natural parts to predict the natural behavior of distorted parts. The predicted parts act as 'reference' and the difference between the reference and distorted parts is used as an indicator to predict the image quality. In order to achieve this goal, we modify the general sparse autoencoder to serve as a predictor to get the predicted parts from natural parts. By translating the NR-IQA problem into classification problem, the predicted parts and distorted parts are utilized to form features and the differences between them are identified by softmax classifier. The resulting algorithm, which we name SHeArlet based No-reference Image quality Assessment (SHANIA), is tested on several database (LIVE, Multiply Distorted LIVE and TID2008

关键词： No-reference image quality assessment Shearlet transform sparse autoencoder Softmax classification

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Post-Processing of Unsupervised Dictionary Learning in Handwritten Digit Recognition 14

Post-Processing of Unsupervised Dictionary Learning in Handw...

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International Symposium on Communications and Information Technologies

作者： Phaisangittisagul, Ekachai Chongprachawat, Rapeepol Kasetsart Univ Fac Engn Dept Elect Engn Bangkok 10900 Thailand Kasetsart Univ Grad Sch Bangkok 10900 Thailand

ISBN: (纸本)9781479944163

To achieve high performance in object recognition, a high-level feature representation is play an essential role to transform a raw input data (low-level) into a new representation. Unsupervised feature learning is one of the most successful methods that is widely used in machine learning literatures for creating a high-level feature to improve the supervised learning problems. The main concept of unsupervised feature learning is, in some sense, to encode input knowledge for gaining not only latent features but compact high-fidelity representation. In particular, a sparse coding has proven to be an effective tool as a prior lead to state-of-the-art performance in many benchmark datasets. In sparse coding, an input data can be represented as a sparse linear combination of a set of training overcomplete dictionary. However, an open problem in sparse coding is how to create and correctly choose the dictionary for representing a given input. Moreover, some bases of the learned dictionary is highly dependent and non-orthogonal among itself. In this study, we propose a post-processing scheme to transform the overcomplete dictionary obtained from unsupervised feature learning using Principle Component Analysis (PCA). The goal of our post-processing is to reduce the number of bases in the unsupervised dictionary learning for real-time applications. In experimental results, while the performance without post-processing the unsupervised dictionary learning is better than that of using PCA, it takes more computation and requires more resources during the test time.

关键词： dictionary high-level feature MNIST dataset sparse autoencoder unlabeled data unsupervised feature learning

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Training Large Scale Deep Neural Networks on the Intel Xeon Phi Many-core Coprocessor 28

Training Large Scale Deep Neural Networks on the Intel Xeon ...

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28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Jin, Lei Wang, Zhaokang Gu, Rong Yuan, Chunfeng Huang, Yihua Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781479941162

As a new area of machine learning research, the deep learning algorithm has attracted a lot of attention from the research community. It may bring human beings to a higher cognitive level of data. Its unsupervised pre-training step allows us to find high-dimensional representations or abstract features which work much better than the principal component analysis (PCA) method. However, it will face problems when being applied to deal with large scale data due to its intensive computation from many levels of training process against large scale data. The sequential deep learning algorithms usually can not finish the computation in an acceptable time. In this paper, we propose a many-core algorithm which is based on a parallel method and is used in the Intel Xeon Phi many-core systems to speed up the unsupervised training process of sparse autoencoder and Restricted Boltzmann Machine (RBM). Using the sequential training algorithm as a baseline to compare, we adopted several optimization methods to parallelize the algorithm. The experimental results show that our fully-optimized algorithm gains more than 300-fold speedup on parallelized sparse autoencoder compared with the original sequential algorithm on the Intel Xeon Phi coprocessor. Also, we ran the fully-optimized code on both the Intel Xeon Phi coprocessor and an expensive Intel Xeon CPU. Our method on the Intel Xeon Phi coprocessor is 7 to 10 times faster than the Intel Xeon CPU for this application. In addition to this, we compared our fully-optimized code on the Intel Xeon Phi with a Matlab code running on single Intel Xeon CPU. Our method on the Intel Xeon Phi runs 16 times faster than the Matlab implementation. The result also suggests that the Intel Xeon Phi can offer an efficient but more general-purposed way to parallelize the deep learning algorithm compared to GPU. It also achieves faster speed with better parallelism than the Intel Xeon CPU.

关键词： Deep learning Unsupervised learning Deep architecture sparse autoencoder Restricted Boltzmann Machine Parallel algorithm Intel Xeon Phi Many-core

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A Novel Deep Model for Image Recognition 5

A Novel Deep Model for Image Recognition

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5th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhu, Ming Wu, Yan Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781479932795

In this paper we propose a hybrid deep network for image recognition. First we use the sparse autoencoder(SAE) which is a method to extract high-level feature representations of data in an unsupervised way, without any manual feature engineering, and then we perform the classification using the deep belief networks(DBNs), which consist of restricted Boltzmann machine(RBM). Finally, we implement some comparative experiments on image datasets, and the results show that our methods achieved better performance when compared with neural network and other deep learning techniques such as DBNs.

关键词： deep belief network sparse autoencoder image recognition

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A Novel Deep Model for Image Recognition

A Novel Deep Model for Image Recognition

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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： Ming Zhu Yan Wu College of Electronics & Information Engineering University of Tongji

In this paper we propose a hybrid deep network for image *** we use the sparse autoencoder(SAE) which is a method to extract high-level feature representations of data in an unsupervised way,without any manual feature engineering,and then we perform the classification using the deep belief networks(DBNs),which consist of restricted Boltzmann machine(RBM).Finally,we implement some comparative experiments on image datasets,and the results show that our methods achieved better performance when compared with neural network and other deep learning techniques such as DBNs.

关键词： deep belief network sparse autoencoder image recognition

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基于三维人脸建模的多视角人脸识别方法研究

基于三维人脸建模的多视角人脸识别方法研究

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作者：邢健飞杭州电子科技大学

学位级别：硕士

作为典型的模式识别任务，人脸识别有着巨大的实际应用价值与市场前景。理想环境下的人脸识别已经取得不俗成绩，然而，当所处环境变化（如姿态变换、夸张表情、阴阳脸、分辨率较低）时，识别难度增加，效果也急剧变差。与此同时，现有... 详细信息

作为典型的模式识别任务，人脸识别有着巨大的实际应用价值与市场前景。理想环境下的人脸识别已经取得不俗成绩，然而，当所处环境变化（如姿态变换、夸张表情、阴阳脸、分辨率较低）时，识别难度增加，效果也急剧变差。与此同时，现有方法大多数无法实时（在线）完成人脸识别任务，这也限制了人脸识别技术的应用。本文在浙江省大学生科技成果推广项目(2013R407063)的赞助下，以人脸识别的背景及目的意义为出发点，考虑到人脸的三维结构，分析研究了基于三维人脸建模的多视角人脸识别方法，本文主要完成了以下几方面的研究工作：（1）人脸检测阶段：为了确保系统的稳定性与实时性，选择了基于AdaBoost的人脸检测算法并将所得的分类器打包成XML文件的形式，为以后的实时人脸识别系统的搭建打下了基础。（2）三维人脸建模方面：在比较几种基于图像的三维人脸建模方法后，选择了基于单张正面的照片的三维人脸建模方法，与常规方法相比，该方法的可行性与实用性高，方法可以无缝链接到人脸识别系统中。本文提出了一套基于Candide-3的三维人脸建模方法，具体分为两步：首先，使用改进的ASM（主动形状模型）算法对定位正面人脸的特征点进行定位并根据临近点插值法确定非特征点坐标；然后，在纹理贴图阶段，本文依据DMS样条函数，在Candide-3标准模型的基础上生成了含更多顶点的人脸模型，使得建立的模型真实感和连续性更好。最后，给出了根据所建三维人脸模型生成的多视角人脸图像。（3）在人脸图像特征提取阶段：本文提出了基于深度神经网络的人脸识别方法，本文的深度神经网络的训练分为两步：无监督阶段与有监督阶段。其中，在无监督阶段，使用sparse autoencoder在大规模人脸库LFW的基础上进行图像恢复以训练深度神经网络。与此同时，为探讨网络层数对于识别效果的影响，本文训练了多种多层数的神经网络，实验结果表明，识别率并非与网络层数正相关；有监督阶段，采用邻近元分析的方法，利用混合人脸库人脸的多样性(多姿态、多表情、多种光照)，有目的地提高网络对于人脸的理解能力，减小相同人脸间距离，增大不同人脸间距离。实验结果表明，较传统方法，本文提出的特征提取算法可提取人脸图像的深度特征，适用于人脸姿态、表情、光照变化较大的人脸识别任务中，对于人脸识别方法的探究是一次有益的尝试。（4）在实际的人脸识别过程中：提出了一种“搜索半径”的概念。首先将每张图像用二进制数据表示。在图像检索过程中，先计算出图像间的二进制数据间的汉明距离，然后按照汉明距离排序，在汉明距离较小的半径内，寻找与待识别图像欧式距离最小的图像，根据阈值，确定该图像是否是待识别个体。实验结果表明，本套识别方法所用识别时间极小，适合于对实时性要求较高的人脸识别任务。（5）最后，本文将所有的算法整合起来，利用OpenCV、OpenGL、Armadillo等库搭建了人脸实时识别系统，并演示了系统的具体操作。实验结果表明，本套人脸系统识别效果好，识别时间短，是人脸识别较为理想的方案。

关键词：人脸识别三维人脸建模 AdaBoost Candide-3 sparse autoencoder 邻近元分析

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SAR Automatic Target Recognition Based on a Visual Cortical System

SAR Automatic Target Recognition Based on a Visual Cortical ...

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6th International Congress on Image and Signal Processing (CISP)

作者： Ni, Jia Cheng Xu, Yue Lei Air Force Engn Univ Inst Aeronaut & Astronaut Engn Xian Peoples R China

ISBN: (纸本)9781479927630

Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.

关键词： SAR Object Recognition Visual Cortex ICM sparse autoencoder

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Receptive Field Resolution Analysis in Convolutional Feature Extraction

Receptive Field Resolution Analysis in Convolutional Feature...

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13th International Symposium on Communications and Information Technologies (ISCIT) - Communication and Information Technology for New Life Style Beyond the Cloud

作者： Phaisangittisagul, Ekachai Chongprachawat, Rapeepol Kasetsart Univ Fac Engn Dept Elect Engn Bangkok 10900 Thailand Kasetsart Univ Grad Sch Bangkok 10900 Thailand

ISBN: (纸本)9781467355803;9781467355780

Instead of introducing new learning algorithm for solving complex classification tasks, many research groups in machine learning have focused on creating a good feature representation. In addition, labeled data is often difficult and expensive to obtain sufficiently large amount of data. So, learning features from unlabeled data is proposed since unlabeled data is much easier to obtain than the labeled data. In this work, a highlevel feature representation is created by a sparse autoencoder with convolutional extraction. A sparse autoencoder is an unsupervised feedforward neural network that is trained to predict the input itself and has been widely used for learning good feature representation. A major advantage of this feature extraction approach not only provides good feature representation for higherlevel tasks but also can scale up to large images. However, there are several parameters that require careful selection to obtain high performance. The main objective in this work is to present a detailed analysis on the effect of receptive field resolution in handwritten classification based on MNIST database. In the experiment, the results show that receptive field resolution is one of the critical parameters to achieve state-of-the-art performance.

关键词： high-level feature labeled data MNIST dataset receptive field resolution sparse autoencoder unlabeled data

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