Feature fusion method based on canonical correlation analysis and handwritten character recognition

2005 ◽  
Author(s):  
Quan-Sen Sun ◽  
Sheng-Gen Zeng ◽  
Pheng-Ann Heng ◽  
De-Sen Xia
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Character Recognition ◽  
Feature Fusion ◽  
Fusion Method ◽  
Handwritten Character Recognition ◽  
Handwritten Character

A New Feature Fusion Method for Handwritten Character Recognition Based on 3D Accelerometer

2010 ◽  
Vol 44-47 ◽  
pp. 1583-1587 ◽  
Author(s):  
Zhen Yu He
Keyword(s):  
Character Recognition ◽  
Feature Fusion ◽  
Consumer Electronics ◽  
Support Vector ◽  
Accelerometer Data ◽  
Fusion Method ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
New Feature ◽  
Fusion Feature

In this paper, a new feature fusion method for Handwritten Character Recognition based on single tri-axis accelerometer has been proposed. The process can be explained as follows: firstly, the short-time energy (STE) features are extracted from accelerometer data. Secondly, the Frequency-domain feature namely Fast Fourier transform Coefficient (FFT) are also extracted. Finally, these two categories features are fused together and the principal component analysis (PCA) is employed to reduce the dimension of the fusion feature. Recognition of the gestures is performed with Multi-class Support Vector Machine. The average recognition results of ten Arabic numerals using the proposed fusion feature are 84.6%, which are better than only using STE or FFT feature. The performance of experimental results show that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile device.

scholarly journals Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

2019 ◽  
Vol 37 (1) ◽  
pp. 87-92
Author(s):  
Honghui Yang ◽  
Shuzhen Yi
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Target Recognition ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Algorithm ◽  
Fusion Method ◽  
Underwater Target

To solve high-dimensional and small-sample-size classification problem for underwater target recognition, a new feature fusion method is proposed based on multi-kernel sparsity preserve multi-set canonical correlation analysis. The multi-set canonical correlation analysis algorithm is used to quantitatively analyze the correlation of multi-domain features, remove redundant and noise features, in order to achieve multi-domain feature fusion. The multi-kernel sparsely preserved projection algorithm is used to constrain the sparse reconstruction of the extracted multi-domain feature samples, which enhances the feature's classification ability. Results of applying real radiated noise datasets to underwater target recognition experiments show that our new method can effectively remove the redundancy and noise features, achieve the fusion of multi-domain underwater target features, and improve the recognition accuracy of underwater targets.

Underwater Sediments Echoes Recognition Based on KECCA + PLS

2013 ◽  
Vol 310 ◽  
pp. 629-633
Author(s):  
Bo Wen Luo ◽  
Bu Yan Wan ◽  
Wei Bin Qin ◽  
Ji Yu Xu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Nuclear Space ◽  
Nonlinear Feature

In order to solve the nonlinear feature fusion of underwater sediments echoes, the shortage of Enhanced Canonical Correlation Analysis (ECCA) was analyzed and made ECCA extend to Kernel ECCA (KECCA) in the nuclear space, a multi-feature nonlinear fusion classification model with KECCA combining with Partial Least-Square (PLS ) was put forward。In the process of identifying four types of underwater sediment such as Basalt, Volcanic breccia, Cobalt crusts and Mudstone, the results showed that the recognition accuracy could be further improved for the KECCA + PLS model.

scholarly journals Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Shi ◽  
Chao Chen ◽  
Hui Liu ◽  
Yinglong Wang ◽  
Minglei Shu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Expert Knowledge ◽  
Feature Fusion ◽  
Intraclass Correlation ◽  
Single Feature ◽  
Disability Rate ◽  
Parallel Feature

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.

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