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Collaborative filtering algorithm via compressing the sparse user-rating-data matrix

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2009年第4期36卷 614-618+638页

作者： Hou, Cui-Qin Jiao, Li-Cheng Zhang, Wen-Ge Ministry of Education Key Lab. of Intelligent Perception and Image Understanding Research Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 China

The paper proposes a novel memory-based collaborative filtering algorithm-Multi-label Probabilistic Latent Semantic Analysis based Collaborative Filtering, which improves the quality of recommendations by reducing the dimension of the user-rating-data matrix by multi-label probabilistic latent semantic analysis when the matrix is extremely sparse. Firstly, it confines the set of latent variables of probability latent semantic analysis to the set of multi-label of items to make latent variables have meanings of corresponding labels. Then it learns the probabilistic distribution of latent variables, i.e., the model of use's interest, to compress the user-rating-data matrix. Finally, it computes the similarity between different users based on the above learned model and makes recommendations. Compared to memory-based collaborative filtering algorithms, the proposed algorithm decreases the mean absolute error 4 percents averagely on test dataset by reducing the dimension of the user-rating-data matrix. The proposed algorithm makes the recommendation system understandable and obtains competitive recommendations compared to the filtering algorithm which reduces the dimension of the user-rating-data matrix by probabilistic latent semantic analysis.

关键词： Iterative methods

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Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration 30

Planning with Learned Dynamic Model for Unsupervised Point C...

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30th International Joint Conference on Artificial Intelligence, IJCAI 2021

作者： Jiang, Haobo Qian, Jianjun Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9780999241196

Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which can seek the transformation between the source and target point clouds through trial and error. By modeling the point cloud registration process as a Markov decision process (MDP), we develop a latent dynamic model of point clouds, consisting of a transformation network and evaluation network. The transformation network aims to predict the new transformed feature of the point cloud after performing a rigid transformation (i.e., action) on it while the evaluation network aims to predict the alignment precision between the transformed source point cloud and target point cloud as the reward signal. Once the dynamic model of the point cloud is trained, we employ the cross-entropy method (CEM) to iteratively update the planning policy by maximizing the rewards in the point cloud registration process. Thus, the optimal policy, i.e., the transformation between the source and target point clouds, can be obtained via gradually narrowing the search space of the transformation. Experimental results on ModelNet40 and 7Scene benchmark datasets demonstrate that our method can yield good registration performance in an unsupervised manner. © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Dynamic models

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An improved spectral clustering ensemble algorithm for POLSAR land cover classification

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International Journal of Earth Sciences and Engineering 2015年第2期8卷 937-943页

作者： Liu, Lu Shi, Junfei Jiao, Licheng Jin, Shaohui Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education Xidian Univ. Xi’an China

In this paper, a Laplacian eigenmap and dictionary learning based spectral clustering ensemble (LE-DLSCE) algorithm for polarimetric SAR (PolSAR) land cover classification is proposed to solve problems of high computational complexity and instability of spectral clustering (SC) *** polarimetric feature and spatial texture feature are combined to obtain the feature matrix of PolSAR image. Laplacian eigenmap (LE) which reduces the data amount processed by SC is then applied to compress the feature matrix. Dictionary learning based sample selection method is used to increase the information involved in sample points and avoid the instability classification results caused by random sampling of Nyström approximation. The Markov Random Field (MRF) potential function is employed to construct the similarity matrix by combined similarity measures. Finally, multiple individual PolSAR classification results are obtained and integrated by an ensemble strategy. The experimental results demonstrate that the proposed method improves the classification performance, having advantages in region homogeneity and edge preservationand achieving stable results. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Synthetic aperture radar

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Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware

Global Spatio-Temporal Fusion-based Traffic Prediction Algor...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Chaoqun Li, Xuanpeng Gong, Chen Li, Guangyu Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security China Southeast University Nanjing210094 China

ISBN: (纸本)9798350351255

Traffic prediction is an indispensable component of urban planning and traffic management. Achieving accurate traffic prediction hinges on the ability to capture the potential spatio-temporal relationships among road sensors. However, the majority of existing works focus on local short-term spatiotemporal correlations, failing to fully consider the interactions of different sensors in the long-term state. In addition, these works do not analyze the influences of anomalous factors, or have insufficient ability to extract personalized features of anomalous factors, which make them ineffectively capture their spatiotemporal influences on traffic prediction. To address the aforementioned issues, We propose a global spatio-temporal fusionbased traffic prediction algorithm that incorporates anomaly awareness. Initially, based on the designed anomaly detection network, we construct an efficient anomalous factors impacting module (AFIM), to evaluate the spatio-temporal impact of unexpected external events on traffic prediction. Furthermore, we propose a multi-scale spatio-temporal feature fusion module (MTSFFL) based on the transformer architecture, to obtain all possible both long and short term correlations among different sensors in a wide-area traffic environment for accurate prediction of traffic flow. Finally, experiments are implemented based on real-scenario public transportation datasets (PEMS04 and PEMS08) to demonstrate that our approach can achieve stateof-the-art performance. © 2024 IEEE.

关键词： Weather forecasting

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Unified Density-Aware image Dehazing and Object Detection in Real-World Hazy Scenes 15th

Unified Density-Aware Image Dehazing and Object Detection in...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhang, Zhengxi Zhao, Liang Liu, Yunan Zhang, Shanshan Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High -Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783030695378

It is an important yet challenging task to detect objects on hazy images in real-world applications. The major challenge comes from low visual quality and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed Unified Dehazing and Detection (UDnD) framework consists of three parts: a residual-aware haze density classifier, a density-aware dehazing network, and a density-aware object detector. First, the classifier exploits the residuals of hazy images to accurately predict density levels, which provide rich domain knowledge for the subsequent two tasks. Then, we design respectively a High-Resolution Dehazing Network (HRDN) and a Faster R-CNN-based multi-domain object detector to leverage the extracted density information and tackle hazy object detection. Experiments demonstrate that UDnD performs favorably against other methods for object detection in real-world hazy scenes. Also, HRDN achieves better results than state-of-the-art dehazing methods in terms of PSNR and SSIM. Hence, HRDN can conduct haze removal effectively, based on which UDnD is able to provide high-quality detection results. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Assignment problem based deep embedding 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Zheng, Ruishen Xie, Jin Qian, Jianjun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

How to measure the similarity of samples is a fundamental problem in many computer vision tasks such as retrieval and clustering. Due to the rapid development of deep neural networks, deep metric learning has been widely studied. Some studies focus on the hard sample mining strategy for triplet loss. We observe that hard mining strategies are also vital for contrastive loss. But the hardest mining strategy for contrastive loss is sensitive to outliers. In this paper, based on combinatorial information of sample pairs, we propose a novel linear assignment problem based hard sample mining strategy for contrastive loss to learn feature embeddings. Specifically, our method can assign 0/1 weight to sample pairs for the hard sample selection by maximizing a linear assignment loss and ensure that each sample is only included by one pair for the optimization. Our method can obtain the state-of-the-art performance on the CUB-200-2011, Cars196, and In-shop datasets with the GoogLeNet network. © Springer Nature Switzerland AG 2019.

关键词： Combinatorial optimization

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Self-Constrained Graph Stochastic Neural Networks for Graphstructure Learning

SSRN

引用

SSRN 2022年

作者： Jia, Nan Tian, Xiaolin Wang, Kai Huang, Xiaocui Key Lab of Intelligent Perception and Image Understanding of Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for graph structure ***, most existing GNNs heavily rely on graph structure and may suffer from the limitation of over-smoothingwhen we enlarge the feature propagation steps. In this paper, a simple yet effective framework—the Self-ConstrainedGraph Stochastic Neural Networks (SCGSNN) is proposed to address these two issues. Firstly, we use the multibranchgenerating structure to learn the adjacency matrix and the GNN parameters simultaneously in SCGSNN,aiming to obtain multiple different first-order adjacency matrices. Then, we develop the self-constrained mechanismto constrain each generation structure branch to infer a more realistic graph structure. Finally, we apply the mixedmulti-level information aggregation to alleviate the issue of over-smoothing. Experimental results show that SCGSNNcan be extended to large graphs with million-level nodes and apparently outperforms state-of-the-art GNNs baselinesfor the node classification task. © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem

arXiv

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arXiv 2023年

作者： Lv, Haoyuan Liu, Ruochen Key Lab of Intelligent Perception and Image Understanding Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Multitasking

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Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation

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Advances in Radiation Oncology 2016年第3期1卷 182-193页

作者： Gou, Shuiping Lee, Percy Hu, Peng Rwigema, Jean-Claude Sheng, Ke Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shanxi China Department of Radiation Oncology University of California Los Angeles California United States Department of Radiological Science University of California Los Angeles California United States

Purpose With the advent of magnetic resonance imaging (MRI) guided radiation therapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods. Methods and materials T2-weighted half-Fourier acquisition single-shot turbo spin-echo and T1 weighted volumetric interpolated breath-hold examination images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging, distance regularized level set, and graph cuts, and the segmentation results were compared with manual contours using Dice's index, Hausdorff distance, and shift of the center of the organ (SHIFT). Results All volumetric interpolated breath-hold examination images were successfully segmented by at least 1 of the autosegmentation method with Dice's index >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of half-Fourier acquisition single-shot turbo spin-echo images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, distance regularized level set and DL performed slightly superior to the graph cuts method, and substantially superior to mean-shift merging. DL required least human supervision and was faster to compute. Conclusions Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization. © 2016 The Authors on behalf of the American Society for Radiation Oncology

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A Sparse Kernel Representation Method for image Classification

A Sparse Kernel Representation Method for Image Classificati...

引用

International Joint Conference on Neural Networks

作者： Shuyuan Yang Yue Han XiangRong Zhang Department of electronic engineering Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xi'an China

ISBN: (纸本)9781467314886

In this paper, we propose a sparse kernel representation classification algorithm (SKRC) for images classification and recognition. The training dictionary is composed by labeled samples directly, and both training dictionary and testing sample are mapped into feature space from original sample space by the sparse kernel which employs the "center" samples matrix constructed by a method similar to k-means clustering. Then in the feature space, the basic sparse representation based classification method is employed. We test our proposed algorithm on some different public database, and the results show that our proposed method can achieve higher classification accuracy without much time consumed.

关键词： Sparse kernel Sparse representation Classification

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