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Robust unsupervised feature selection by nonnegative sparse subspace learning

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Food recommendation: Framework, existing solutions and challenges

arXiv

引用

arXiv 2019年

作者： Min, Weiqing Jiang, Shuqiang Jain, Ramesh Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of California IrvineCA United States

A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elab.rate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field. Copyright © 2019, The Authors. All rights reserved.

关键词： Domain Knowledge

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Measurement of Absolute Depth of the Objects in Images Based on SURF Feature

Measurement of Absolute Depth of the Objects in Images Based...

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2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018

作者： Lixin, He Can, Wang Bin, Kong Jing, Yang CAS Hefei Research Center for Biomimetic Vision Hefei Institute of Intelligent Machines Anhui China Department of Automation University of Science and Technology of China Hefei Anhui China Key Lab of Network and Intelligent Information Processing Hefei University Hefei Anhui China

ISBN: (纸本)9781538660577

A method of depth measurement based on SURF without camera calibration and parameter adjustment is presented in this paper. In this method, two images of a same scene are captured by a monocular camera. Then the objects in the two images are matched automatically after the images have been segmented and SURF (Speeded Up Robust Features) points have been extracted from the images respectively. Finally, a pair of straight line segments in the matched objects is chose and depth of the object is calculated according to the lengths of the pair of straight line segments. The results of the experiments show the method is effective. © 2018 IEEE.

关键词： Cameras

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Efficient and adaptive kernelization for nonlinear max-margin multi-view learning

arXiv

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arXiv 2019年

作者： Du, Changying Jia He, B. Du, Changde Zhuang, Fuzhen He, Qing Long, Guoping Huawei Noah's Ark Lab Beijing100085 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China Research Center for Brain-Inspired Intelligence Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing100049 China

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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de novo sequencing with mirror image protease of LysargiNase and trypsin for large scale proteomics

de novo sequencing with mirror image protease of LysargiNase...

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中国生物化学与分子生物学会第十二届全国会员代表大会暨2018年全国学术会议

作者：徐平杨皓李衍常赵明治张俊令王一豪迟浩贺思敏 National Center for Protein Sciences Beijing Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing TechnologyCAS

De novo peptide sequencing for an individual pure protein has improved remarkably with the progress of mass spectrometry but there still exists incomplete peptide fragmentation and indistingushiable series of fragmented ions,which interrupted the interpretation of amino acid sequence of *** is even worse at proteome ***,we developed a series of high efficiency proteases,including LysargiNase with super activity and acetylated trypsin with high *** advantage of these two enzymes,we developed a novel algorithm,pNovoM for automated de novo sequencing of the mirrored peptides generated from the digestion of trypsin and LysargiNase *** combination of paired mirror image spectra results nearly complete series of product ions,which facilitate the de novo sequencing on both of purified protein and proteasome samples.

关键词： de novo sequencing proteomics mirror enzyme LysargiNase

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Harmonized multimodal learning with Gaussian process latent variable models

arXiv

引用

arXiv 2019年

作者： Song, Guoli Wang, Shuhui Huang, Qingming Tian, Qi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Noah's Ark Lab Shenzhen518129 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs) to represent multimodal data in a common space. Previous multimodal GPLVM extensions generally adopt individual learning schemes on latent representations and kernel hyperparameters, which ignore their intrinsic relationship. To exploit strong complementarity among different modalities and GPLVM components, we develop a novel learning scheme called Harmonization, where latent model parameters are jointly learned from each other. Beyond the correlation fitting or intra-modal structure preservation paradigms widely used in existing studies, the harmonization is derived in a model-driven manner to encourage the agreement between modality-specific GP kernels and the similarity of latent representations. We present a range of multimodal learning models by incorporating the harmonization mechanism into several representative GPLVM-based approaches. Experimental results on four benchmark datasets show that the proposed models outperform the strong baselines for cross-modal retrieval tasks, and that the harmonized multimodal learning method is superior in discovering semantically consistent latent representation. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Scene Text Detection via Deep Semantic Feature Fusion and Attention-based Refinement

Scene Text Detection via Deep Semantic Feature Fusion and At...

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International Conference on Pattern Recognition

作者： Yu Song Yuanshun Cui Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Despite tremendous progress in scene text detection in the past few years, efficient text detection in the wild remains challenging, particularly for the texts have large rotations, and the complicated background areas that are easily confused with text. In this paper, we propose an effective approach for scene text detection, which consists of initial text detection using the proposed deep semantic feature fusion of a fully convolutional network (FCN), and text detection refinement by our attention based text vs. non-text classifier learned in a fine-to-coarse fashion. The proposed approach outperforms the state-of-the-art scene text detection algorithms on the public-domain ICDAR2015 dataset, achieving an accuracy of 0.83 in terms of F-measure.

关键词： Feature extraction Semantics Text recognition Proposals Task analysis Object detection

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Review of intelligent computing application 1st

Review of intelligent computing application

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Wang, Yiou Liu, Tianyuan Zhang, Fuquan Xu, Lin Ding, Gangyi Xiong, Rui Liu, Fei Beijing Institute of Science and Technology Information Beijing100044 China Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Digital Performance and Simulation Technology Lab. School of Software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

intelligent computing systems can automatically sense environmental changes in the sensor network, make judgments and prediction on the environmental status in time, and provide response strategies in different environments, applying some technologies of pattern recognition, time series prediction and big data analytics. Nowadays, intelligent computing is successfully used in a lot of areas such as transportation, healthcare, home, performance and environmental monitoring. In this paper, the concept of intelligent computing is introduced. Then, the applications in different areas are discussed detailedly. Finally, the future of intelligent computing is analyzed and the conclusion is briefly given. © Springer International Publishing AG 2018.

关键词： Wireless sensor networks

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The propositional normal default logic and the finite/infinite injury priority method

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Science China(information Sciences) 2017年第9期60卷 99-108页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

In propositional normal default logic, given a default theory(?, D) and a well-defined ordering of D, there is a method to construct an extension of(?, D) without any injury. To construct a strong extension of(?, D) given a well-defined ordering of D, there may be finite injuries for a default δ∈ D. With approximation deduction ?s in propositional logic, we will show that to construct an extension of(?, D) under a given welldefined ordering of D, there may be infinite injuries for some default δ∈ D.

关键词： default extension strong extension finite/infinite injury priority method recursively enumerable sets

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The B4-valued propositional logic with unary logical connectives ～1 /～2/┐

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Frontiers of Computer Science 2017年第5期11卷 887-894页

作者： Wei LI Yuefei SUI State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astronautics Beijing 100191 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

A B4-valued propositional logic will be proposed in this paper which there are three unary logical connectives ～1, ～2, ┐ and two binary logical connectives A, v, and a Gentzen-typed deduction system will be given s... 详细信息

关键词： the Belnap logic modality the soundness thecompleteness

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