Search Results for author: Jim Jing-Yan Wang

Found 18 papers, 0 papers with code

Cross-model convolutional neural network for multiple modality data representation

no code implementations19 Nov 2016 Yanbin Wu, Li Wang, Fan Cui, Hongbin Zhai, Baoming Dong, Jim Jing-Yan Wang

A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities.

Semi-supervised structured output prediction by local linear regression and sub-gradient descent

no code implementations7 Jun 2016 Ru-Ze Liang, Wei Xie, Weizhi Li, Xin Du, Jim Jing-Yan Wang, Jingbin Wang

The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.

Structured Prediction

Sparse Coding with Earth Mover's Distance for Multi-Instance Histogram Representation

no code implementations9 Feb 2015 Mohua Zhang, Jianhua Peng, Xuejie Liu, Jim Jing-Yan Wang

It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error.

Regularized maximum correntropy machine

no code implementations18 Jan 2015 Jim Jing-Yan Wang, Yunji Wang, Bing-Yi Jing, Xin Gao

To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework.

Multi-view learning for multivariate performance measures optimization

no code implementations15 Jan 2015 Jim Jing-Yan Wang

We propose to learn a linear discriminant functions for each view, and combine them to construct a overall multivariate mapping function for mult-view data.


Learning manifold to regularize nonnegative matrix factorization

no code implementations3 Oct 2014 Jim Jing-Yan Wang, Xin Gao

Recently, manifold regularized NMF used a nearest neighbor graph to regulate the learning of factorization parameter matrices and has shown its advantage over traditional NMF methods for data representation problems.

feature selection graph construction +2

Maximum mutual information regularized classification

no code implementations27 Sep 2014 Jim Jing-Yan Wang, Yi Wang, Shiguang Zhao, Xin Gao

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label.

Classification General Classification

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

no code implementations Elsevier Ltd 2014 Jim Jing-Yan Wang, Jianhua Z. Huang, Yijun Sun, Xin Gao

To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively.

feature selection

When coding meets ranking: A joint framework based on local learning

no code implementations8 Sep 2014 Jim Jing-Yan Wang, Xuefeng Cui, Ge Yu, Lili Guo, Xin Gao

In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm.

Domain Transfer Structured Output Learning

no code implementations3 Sep 2014 Jim Jing-Yan Wang

The problem is to learn a predictor for the target domain to predict the structured outputs from the input.

Large Margin Image Set Representation and Classification

no code implementations22 Apr 2014 Jim Jing-Yan Wang, Majed Alzahrani, Xin Gao

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets.

Classification Face Recognition +1

Max-Min Distance Nonnegative Matrix Factorization

no code implementations5 Dec 2013 Jim Jing-Yan Wang

It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basic matrix and a nonnegative coefficient matrix, and the coefficient matrix is used as the new representation.

Cross-Domain Sparse Coding

no code implementations27 Nov 2013 Jim Jing-Yan Wang

Sparse coding has shown its power as an effective data representation method.

Image Classification Spam detection

Semi-Supervised Sparse Coding

no code implementations26 Nov 2013 Jim Jing-Yan Wang, Xin Gao

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations.

Multiple graph regularized protein domain ranking

no code implementations18 Aug 2012 Jim Jing-Yan Wang, Halima Bensmail, Xin Gao

However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.

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