A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection

Yeh, Yi-Ren; Lin, Ting-Chu; Chung, Yung-Yu; Wang, Yu-Chiang Frank

doi:10.1109/tmm.2012.2188783

Cited by 65 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Feature fusion Yeh et al [18] and Pong and Lam [19] aims to get the optimal combination of eigenvectors of multiple types and dimensions. The fused feature group should fully demonstrate the complementarity of information, and eliminate redundant data to enhance real-time performance.…”

Section: Multi-feature Motion Posture Recognition Modelmentioning

confidence: 99%

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Yuan-guo¹,

Wu²

2021

View full text Add to dashboard Cite

The effect of motion posture recognition hinges on the accurate description of motion postures with effective feature information. This study introduces Wronskian function to improve the denoising ability of visual background extractor (ViBe) algorithm, and thus acquires relatively clear motion targets. Then, a multi-feature fusion motion posture feature model was developed based on genetic algorithm (GA). Specifically, GA was called to optimize and fuse the extracted feature information, while a fitness function was constructed based on the mean variance ratio, and used to select the feature information with high inter-class discriminability. Taking support vector machine (SVM) as the classifier, a multi-class classifier was designed by one-to-one method for the classification and recognition of motion postures. Through experiments, our model was proved highly accurate in motion posture recognition.

show abstract

Section: Multi-feature Motion Posture Recognition Modelmentioning

confidence: 99%

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

Yuan-guo¹,

Wu²

2021

View full text Add to dashboard Cite

show abstract

“…Yang et al [42] proposed a group-sensitive multiple kernel learning (GS-MKL) method for object recognition to accommodate intraclass diversity and interclass correlation. Similarly, Yeh et al [43] proposed a novel MKL algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection. Althloothi et al [44] used a MKL method to fuse two sets of features, namely shape representation and kinematic structure features, for human activity recognition using a sequence of RGB-D images.…”

Section: Mkl In Biometric Identificationmentioning

confidence: 99%

User Identification Using Real Environmental Human Computer Interaction Behavior

Wu¹,

Zheng²,

Wu³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

show abstract

“…Experimental results show that this method is superior to traditional methods. Yeh studied the image classification problem of audio and video, using the fusion of the Mel-frequency cepstral coefficients (MFCC) feature, scale invariant feature transform (SIFT) descriptor subfeatures, histogram of oriented gradients (HOG) descriptor subfeatures, Gabor texture features, and edge direction histogram (EDH) described characteristics, and then proposed a multicore learning framework which is based on Group Lasso for feature selection [17]. Xie studied the problem with selection of uncertainty characteristics based on Sparse Group Lasso for data mining and has done experiments on nine types of UCI machine learning datasets [18].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Wang

Xue

2015

BioMed Research International

View full text Add to dashboard Cite

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

show abstract

A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection

Cited by 65 publications

References 26 publications

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

User Identification Using Real Environmental Human Computer Interaction Behavior

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Contact Info

Product

Resources

About