Search results for: Songcan Chen

Items from 1 to 20 out of 167 results

article

A Primal Framework for Indefinite Kernel Learning

Hui Xue, Lin Wang, Songcan Chen, Yunyun Wang

Neural Processing Letters > 2019 > 50 > 1 > 165-188

Kernel methods have been widely applied in machine learning to solve complex nonlinear problems. Kernel selection is one of the key issues in kernel methods, since it is vital for improving generalization performance. Traditionally, the selection of kernel is restricted to be positive definite which makes their applicability partially limited. Actually, in many real applications such as gene identification...

article

2D compressed learning: support matrix machine with bilinear random projections

Di Ma, Songcan Chen

Machine Learning > 2019 > 108 > 12 > 2035-2060

Support matrix machine (SMM) is an efficient matrix classification method that can leverage the structure information within the matrix to improve the classification performance. However, its computational and storage costs are still expensive for high-dimensional data. To address these problems, in this paper, we consider a 2D compressed learning paradigm to learn the SMM classifier in some compressed...

article

Ordinal factorization machine with hierarchical sparsity

Shaocheng Guo, Songcan Chen, Qing Tian

Frontiers of Computer Science > 2020 > 14 > 1 > 67-83

Ordinal regression (OR) or classification is a machine learning paradigm for ordinal labels. To date, there have been a variety of methods proposed including kernel based and neural network based methods with significant performance. However, existing OR methods rarely consider latent structures of given data, particularly the interaction among covariates, thus losing interpretability to some extent...

article

Robust ordinal regression induced by lp-centroid

Qing Tian, Wenqiang Zhang, Liping Wang, Songcan Chen, more

Neurocomputing > 2018 > 313 > C > 184-195

Ordinal regression (OR) is an important research topic in machine learning and has attracted extensive attention due to its wide applications. So far, a variety of methods have been proposed to perform OR, in which the class-center-induced threshold methods (like KDLOR and MOR) have received more attention, for their simplicity and promising performance. The class-center-induced ORs typically calculate...

article

Data-driven graph construction and graph learning: A review

Lishan Qiao, Limei Zhang, Songcan Chen, Dinggang Shen

Neurocomputing > 2018 > 312 > C > 336-351

A graph is one of important mathematical tools to describe ubiquitous relations. In the classical graph theory and some applications, graphs are generally provided in advance, or can at least be defined clearly. Thus, the main focus is to measure/analyze the graphs for mining informative patterns. However, for many real-world scenarios, the graph is often uncertain due to the fact that data associated...

article

Cross modal similarity learning with active queries

Nengneng Gao, Sheng-Jun Huang, Yifan Yan, Songcan Chen

Pattern Recognition > 2018 > 75 > C > 214-222

In real applications, data is usually collected from heterogeneous sources and represented with multiple modalities. To facilitate the analysis of such complex tasks, it is important to learn an effective similarity across different modalities. Existing similarity learning methods usually requires a large number of labeled training examples, leading to high labeling costs. In this paper, we propose...

article

Hierarchical Gaussian Processes model for multi-task learning

Ping Li, Songcan Chen

Pattern Recognition > 2018 > 74 > C > 134-144

Multi-task learning (MTL) has been proved to improve performance of individual tasks by learning multiple related tasks together. Recently Nonparametric Bayesian Gaussian Process (GP) models have also been adapted to MTL and exhibit enough flexibility due to its non-parametric nature, thus can exempt from the assumption about the probability distributions of variables. To date, there have had two...

article

Multi-dimensional classification via a metric approach

Zhongchen Ma, Songcan Chen

Neurocomputing > 2018 > 275 > C > 1121-1131

Multi-dimensional classification (MDC) refers to learning an association between individual inputs and their multiple dimensional output discrete variables, and is thus more general than multi-class classification (MCC) and multi-label classification (MLC). One of the core goals of MDC is to model output structure for improving classification performance. To this end, one effective strategy is to...

article

Semi-supervised manifold regularization with adaptive graph construction

Yunyun Wang, Yan Meng, Yun Li, Songcan Chen, more

Pattern Recognition Letters > 2017 > 98 > C > 90-95

Manifold regularization (MR) provides a powerful framework for semi-supervised classification (SSC) learning. It imposes the smoothness constraint over a constructed manifold graph, and its performance largely depends on such graph. However, 1) The manifold graph is usually pre-constructed before classification, and fixed during the classification learning process. As a result, independent with the...

article

Cross-heterogeneous-database age estimation through correlation representation learning

Qing Tian, Songcan Chen

Neurocomputing > 2017 > 238 > C > 286-295

Human age estimation is an important research topic and has found its applications in such scenarios as commodity recommendation and security monitoring. The design of existing estimators generally follows a same pipeline, i.e., an estimator is built from a given training dataset and then evaluated on a holdout testing set from the same dataset to display its effectiveness. In doing so, an implicit...

article

Joint representation classification for collective face recognition

Liping Wang, Songcan Chen

Pattern Recognition > 2017 > 63 > C > 182-192

In recent years, many representation based classifications have been proposed and widely used in face recognition. However, these methods code and classify testing images separately even for image-set of the same subject. This scheme utilizes only an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given...

article

Does organically produced lettuce harbor higher abundance of antibiotic resistance genes than conventionally produced?

Bokai Zhu, Qinglin Chen, Songcan Chen, Yong-Guan Zhu

Environment International > 2017 > 98 > C > 152-159

The demand for organic food products, especially for organic vegetables has been growing rapidly in the last few decades. However, the risk of introducing more antibiotic resistant genes (ARGs) and antibiotic resistant bacteria (ARB) to the vegetables by organic production procedures has long been overlooked. In our study, by using high-throughput quantitative PCR and 16sRNA Illumina sequencing technology,...

chapter

Cross-heterogeneous-database age estimation with co-representation among them

Qing Tian, Songcan Chen

2016 23rd International Conference on Pattern Recognition (ICPR) > 1333 - 1338

2016 23rd International Conference on Pattern Recognition (ICPR)

Human age estimation is an important research topic and can find its applications in such as commodity recommendation and security monitoring. The establishment of existing estimators basically follows a same pipeline, i.e., an estimator is built from a given training dataset like FG-NET and then evaluated on a holdout testing set to determine its effectiveness. In doing so, a usually-followed assumption...

article

Mixed bi-subject kinship verification via multi-view multi-task learning

Xiaoqian Qin, Xiaoyang Tan, Songcan Chen

Neurocomputing > 2016 > 214 > C > 350-357

Bi-subject kinship verification addresses the problem of verifying whether there exists some kind of kin relationship (i.e., father–son, father-daughter, mother–son and mother–daughter) between a pair of parent–child subjects based purely on their visual appearance. The task is challenging due to the involvement of two different subjects possibly with different genders and ages. In addition, collecting...

article

Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification

Mingxia Liu, Daoqiang Zhang, Songcan Chen, Hui Xue

IEEE Transactions on Pattern Analysis and Machine Intelligence > 2016 > 38 > 11 > 2335 - 2341

Error-correcting output coding (ECOC) is one of the most widely used strategies for dealing with multi-class problems by decomposing the original multi-class problem into a series of binary sub-problems. In traditional ECOC-based methods, binary classifiers corresponding to those sub-problems are usually trained separately without considering the relationships among these classifiers. However, as...

article

Ordinal margin metric learning and its extension for cross-distribution image data

Qing Tian, Songcan Chen, Lishan Qiao

Information Sciences > 2016 > 349-350 > C > 50-64

In machine learning and computer vision fields, a wide range of applications, such as human age estimation and head pose recognition, are related to ordinal data in which there exists an order relationship. To perform such ordinal estimations in a desired metric space, in this paper we first propose a novel ordinal margin metric learning (ORMML) method by separating the data classes with a sequence...

article

A robust multi-class AdaBoost algorithm for mislabeled noisy data

Bo Sun, Songcan Chen, Jiandong Wang, Haiyan Chen

Knowledge-Based Systems > 2016 > 102 > C > 87-102

AdaBoost has been theoretically and empirically proved to be a very successful ensemble learning algorithm, which iteratively generates a set of diverse weak learners and combines their outputs using the weighted majority voting rule as the final decision. However, in some cases, AdaBoost leads to overfitting especially for mislabeled noisy training examples, resulting in both its degraded generalization...

chapter

Single Image Subspace for Face Recognition

Jun Liu, Songcan Chen, Zhi-Hua Zhou, Xiaoyang Tan

Lecture Notes in Computer Science > Analysis and Modeling of Faces and Gestures > Poster - II > 205-219

Small sample size and severe facial variation are two challenging problems for face recognition. In this paper, we propose the SIS (Single Image Subspace) approach to address these two problems. To deal with the former one, we represent each single image as a subspace spanned by its synthesized (shifted) samples, and employ a newly designed subspace distance metric to measure the distance of subspaces...

chapter

Fuzzy-Kernel Learning Vector Quantization

Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou

Lecture Notes in Computer Science > Advances in Neural Networks – ISNN 2004 > Learning and Optimization > 180-185

This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the well-known fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results show...

chapter

Clustering Using Normalized Path-Based Metric

Jundi Ding, Runing Ma, Songcan Chen, Jingyu Yang

Lecture Notes in Computer Science > Advances in Neural Networks - ISNN 2008 > Machine Learning and Data Mining > 57-66

In this paper, we propose a normalized path-based metric based on an introduced neighborhood density index which can sufficiently exploit the local density “revealed” by data. The metric axioms (positive definite property, symmetry and triangular inequality) are strictly proved in theory. Using this idea of path, we further devise a heuristic clustering algorithm which can perform the elongated structure...

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INFONA - science communication portal

Search results for: Songcan Chen

A Primal Framework for Indefinite Kernel Learning

2D compressed learning: support matrix machine with bilinear random projections

Ordinal factorization machine with hierarchical sparsity

Robust ordinal regression induced by lp-centroid

Data-driven graph construction and graph learning: A review

Cross modal similarity learning with active queries

Hierarchical Gaussian Processes model for multi-task learning

Multi-dimensional classification via a metric approach

Semi-supervised manifold regularization with adaptive graph construction

Cross-heterogeneous-database age estimation through correlation representation learning

Joint representation classification for collective face recognition

Does organically produced lettuce harbor higher abundance of antibiotic resistance genes than conventionally produced?

Cross-heterogeneous-database age estimation with co-representation among them

Mixed bi-subject kinship verification via multi-view multi-task learning

Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification

Ordinal margin metric learning and its extension for cross-distribution image data

A robust multi-class AdaBoost algorithm for mislabeled noisy data

Single Image Subspace for Face Recognition

Fuzzy-Kernel Learning Vector Quantization

Clustering Using Normalized Path-Based Metric

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