musk and Parkinson-Disease

musk has been researched along with Parkinson-Disease* in 1 studies

Other Studies

1 other study(ies) available for musk and Parkinson-Disease

Article	Year
Mutual information-based Fisher discriminant analysis for feature extraction and recognition with applications to medical diagnosis. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2010, Volume: 2010 This paper presents a novel discriminant analysis (DA) for feature extraction using mutual information (MI) and Fisher discriminant analysis (MI-FDA). Most DA algorithms for feature extraction are based on a transformation which maximizes the between-class scatter and minimizes the within-class scatter. In contrast, the proposed method uses the Fisher's criterion to find a transformation that maximizes the MI between the transferred features and the target classes and minimizes the redundancy. The performance of the proposed method is evaluated using UCI databases and compared with the performance of some DA-based algorithms. The results indicate that MI-FDA provides a robust performance over different data sets with different characteristics. On average, an accuracy rate of 81.3% was achieved using MI-FDA. Topics: Algorithms; Breast Neoplasms; Databases, Factual; Diagnostic Techniques and Procedures; Discriminant Analysis; Ethnicity; Fatty Acids, Monounsaturated; Female; Heart; Humans; Lung Neoplasms; Parkinson Disease; Pattern Recognition, Automated; Survival Analysis; Tomography, Emission-Computed, Single-Photon; Young Adult	2010

Article

Year

Mutual information-based Fisher discriminant analysis for feature extraction and recognition with applications to medical diagnosis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2010, Volume: 2010

This paper presents a novel discriminant analysis (DA) for feature extraction using mutual information (MI) and Fisher discriminant analysis (MI-FDA). Most DA algorithms for feature extraction are based on a transformation which maximizes the between-class scatter and minimizes the within-class scatter. In contrast, the proposed method uses the Fisher's criterion to find a transformation that maximizes the MI between the transferred features and the target classes and minimizes the redundancy. The performance of the proposed method is evaluated using UCI databases and compared with the performance of some DA-based algorithms. The results indicate that MI-FDA provides a robust performance over different data sets with different characteristics. On average, an accuracy rate of 81.3% was achieved using MI-FDA.

Topics: Algorithms; Breast Neoplasms; Databases, Factual; Diagnostic Techniques and Procedures; Discriminant Analysis; Ethnicity; Fatty Acids, Monounsaturated; Female; Heart; Humans; Lung Neoplasms; Parkinson Disease; Pattern Recognition, Automated; Survival Analysis; Tomography, Emission-Computed, Single-Photon; Young Adult

2010