Jia Wei

South China University of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Jiabing Wang

Taizhou University

Qianli Ma

South China University of Technology

Guihua Wen

South China University of Technology

Hong Peng

Guangdong Academy of Sciences

Xianfa Cai

Guangdong Pharmaceutical University

Zhiwen Yu

South China University of Technology

Tolga Taşdizen

University of Utah

Xuan Wang

Harbin Institute of Technology

Wenguang Yuan

Huawei Technologies (China)

Rui Li

Rochester Institute of Technology

Cooperative Institutions

South China University of Technology

Chinese Academy of Sciences

Northwestern Polytechnical University

Peking University

Hefei University of Technology

Central South University

Xidian University

Xi'an Jiaotong University

University of Science and Technology of China

National University of Defense Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Semi-supervised dimensionality reduction based on local estimation error

International Journal of High Performance Computing and Networking (2019)

Xianfa Cai Jia Wei Guihua Wen Zhiwen Yu Yongming Cai

The construction of a graph is extremely important in graph-based semi-supervised learning. However, it is unstable by virtue of sensitivity to the selection of neighbourhood parameter and inaccuracy of the edge weights. Inspired by the good performance of the local learning method, this paper proposes a semi-supervised dimensionality reduction based on local estimation error (LEESSDR) algorithm by utilising local learning projections (LLP) to semi-supervised dimensionality reduction. The algorithm sets the edge weights through minimising the local estimation error and can effectively preserve the global geometric structure as well as the local one of the data. Since LLP does not require its input space to be locally linear, even if it is nonlinear, LLP maps it to the feature space by using kernel functions and then obtains its local estimation error in the feature space. The effectiveness of the proposed method is verified on two popular face databases with promising classification accuracy and favourable robustness.

Robustness

Neighbourhood (mathematics)

Feature vector

10.1504/ijhpcn.2019.099745

Cite

Citations (1)

A Multi Graphs Based Transductive Ensemble Classification Method

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY (2011)

Guoxian Yu Guoji Zhang Jia Wei Yazhou Ren

基于图的直推分类器依赖于图结构。高维数据通常具有冗余和噪声特征，在其上构造的图不能充分反映数据的分布信息，分类器性能因此下降。为此，该文提出一种多图构建方法并把它应用到直推分类中。该方法首先生成多个随机子空间并在每个子空间上进行半监督判别分析，其次在每个判别子空间上构造图并训练一个直推分类器，最后投票融合这些分类器为一个集成分类器。实验结果表明，对比其它直推分类器，该文的集成分类器具有分类正确率高、对参数鲁棒等特点。

10.3724/sp.j.1146.2010.01424

Cite

Citations (1)

Inter-Slice Image Augmentation Based on Frame Interpolation for Boosting Medical Image Segmentation Accuracy

Frontiers in artificial intelligence and applications (2020)

Zhaotao Wu Jia Wei Wenguang Yuan Jiabing Wang Tolga Taşdizen

We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number but also the information of training samples. For this purpose, we propose a frame-interpolation-based data augmentation method to generate intermediate medical images and the corresponding segmentation labels between two consecutive images. We train and test a supervised U-Net liver segmentation network on SLIVER07 and CHAOS2019, respectively, with the augmented training samples, and obtain segmentation scores exhibiting significant improvement compared to the conventional augmentation methods.

Boosting

Image scaling

Interpolation

10.3233/faia200314

Cite

Citations (4)

Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing

International Journal of Machine Learning and Cybernetics (2015)

Meng Meng Jia Wei Jiabing Wang Qianli Ma Xuan Wang

10.1007/s13042-015-0380-3

Cite

Citations (14)

A Keyword Based Prototype for Web Search Result Diversification

Journal of information science and engineering (2012)

Guli Lin Hong Peng Qianli Ma Jia Wei Jiang-Wei Qin

In web search scenario, users often submit short query terms to search engines, expecting to find their desired information in top ranked results. But their queries are so ambiguous that their actual information needs are often unspecified. To satisfy the different information needs, an effective approach is to diversify the top results retrieved for the query. In this paper, we reduce the diversification problem into optimizing the maximum coverage of information facets related to the query, and introduce KED, a novel keyword based prototype for web search result diversification that provides a diverse ranking by selecting documents to cover keywords which belong to different facets underlying the retrieved documents. We evaluated the effectiveness of KED using two public test collections with different kinds of documents. The experiment results show that KED can stably outperform other existing implicit diversification approaches in promoting diversity of top ranked results. Moreover, we show that its effectiveness can be further improved by using high quality keywords.

10.6688/jise.2012.28.3.11

Cite

Citations (1)

A Neighborhood Preserving Based Semi-supervised Dimensionality Reduction Method for Cancer Classification

Advances in intelligent and soft computing (2011)

Xianfa Cai Jia Wei Guihua Wen Jie Li

Feature (linguistics)

Supervised Learning

10.1007/978-3-642-25664-6_4

Cite

Citations (0)

Unveiling Higher-Order Topology via Polarized Topological Charges

arXiv (Cornell University) (2024)

Jia Wei Bao‐Zong Wang Ming-Jian Gao Jun‐Hong An

Real-space topological invariants were widely used to characterize chiral-symmetric higher-order topological phases (HOTPs). However, a momentum-space characterization to these HOTPs, which essentially reveals their intrinsic bulk-boundary correspondence and facilitates their detection in quantum simulation systems, is still lacking. Here, we propose an experimentally observable momentum-space characterization to the chiral-symmetric HOTPs by the concept of polarized topological charges. It makes a unified description to the HOTPs caused by the closing and reopening of band gap not only of the bulk states but also the edge states. Remarkably, these polarized topological charges can also be identified by measuring the pseudospin structures. A feasible scheme to detect the HOTPs in the $^{87}$Rb atomic system is given. Our work opens an avenue for characterization and experimental detection of the HOTPs in momentum space.

10.48550/arxiv.2405.05505

Cite

Citations (0)

Perceptually motivated morphological strategies for shape retrieval

Pattern Recognition (2012)

Rong-Xiang Hu Jia Wei Yang Zhao Jie Gui

10.1016/j.patcog.2012.02.020

Cite

Citations (41)

Integrating Local and Global Manifold structures for unsupervised dimensionality reduction

2022 International Joint Conference on Neural Networks (IJCNN) (2014)

Xiaochen Chen Jia Wei Jinhai Li Xiaodong Zhang

Recently there has been a lot of interest in geometrically motivated approaches dealing with data in high dimensional spaces. We consider the case where data is sampled from a low dimensional manifold which is embedded in high dimensional Euclidean space. In this paper, we propose a novel unsupervised linear subspace learning algorithm called Local and Global Manifold Preserving Embedding (LGMPE). Different from existing manifold learning based linear subspace learning algorithms which aims at preserving either single kind of local manifold structure or single kind of global manifold structure on the data manifold, LGMPE can preserve different local and global manifold structures simultaneously in the graph embedding framework. Several experiments on real face datasets demonstrate the effectiveness of the proposed algorithm.

Manifold alignment

Manifold (fluid mechanics)

Semidefinite embedding

10.1109/ijcnn.2014.6889381

Cite

Citations (5)

Multi-step Bridged Refinement for classifying cross-domian data

International Conference on Machine Learning and Cybernetics (2010)

Jiang-Wei Qin Hong Peng Liang Peng Qianli Ma Jia Wei

Traditional approaches for classification require that the labeled data should have an identical distribution with the unlabeled data in order to build a reliable classifier. However, the identical distributed labeled data are often in short supply. In this situation, we may hope to borrow the labeled data in the relative domain to help the target task. To address this problem, we propose a transfer learning approach called Multi-step Bridged Refinement, which extends the Bridged Refinement algorithm. In the proposed method, we construct a series mixture models to bridge the source and target domain, through which the initial labels of the unlabeled data are refined towards the target distribution in a multi-step way. We empirically show that the proposed method can better discover the relatedness between domains and make better transfer. The result also shows the final accuracy is insensitive to the initial predicted labels as long as the refinement step is sufficient enough.

Labeled data

Transfer of learning

10.1109/icmlc.2010.5580734

Cite

Citations (0)