Improved support vector classification using PCA and ICA feature space modification

Fortuna, J.; Capson, David W.

doi:10.1016/j.patcog.2003.11.009

Cited by 54 publications

(24 citation statements)

References 16 publications

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“…However, tests showed that the other three datasets did not follow a normal distribution, and the PCA and kPCA methods were not able to successfully classify them. The PCA method also relies on calculating the largest variance in order to determine which components are used, but these are not necessarily the directions of maximum discrimination as there is no attempt to use class information, such as within-class scatter [8,48]. The poor performance of these methods on the Brodatz, Fractal and Abraded datasets means they are not suitable for the classification of tribological surfaces.…”

Section: Classifiers Using Pca and Kpca Methodsmentioning

confidence: 99%

Evaluation of methods for reduction of surface texture features

Stachowiak

Podsiadło

2006

Tribol Lett

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The diagnosis of worn and damaged surfaces is an important issue in machine failure analysis and condition monitoring. Of many approaches used, image classification based on feature parameters has often proven to be particularly useful. However, large image databases can be computationally costly to analyse, and the datasets are susceptible to noise. Hence, it is essential to determine which feature parameters hold the most useful information, in order to improve the classification rate and computation time. This paper presents a performance evaluation of dimension reduction techniques currently used in pattern recognition. A comparison of three methods is conducted, in order to determine which is able to produce the best results over a large range of image datasets. The methods analysed are: Non-Linear Fisher, Principal Component Analysis and kernel Principal Component Analysis. These are then tested against four different classifiers to obtain the best combination. These classifiers are: Linear Discriminant Classifier, Quadratic Discriminant Classifier, k-Nearest Neighbour and Support Vector Machine Classifiers. For further analysis, two combined dimension reduction and classification methods are tested: Minimum Classification Error and reduced feature space Support Vectors. For the comparison, four datasets of images with different scales and rotations are used, i.e. Brodatz textures, artificially generated isotropic fractal images and Talysurf images of sandblasted and abraded steel surfaces. The results showed that a combination of the Non-Linear Fisher dimension reduction technique and a Linear Support Vector Machine Classifier gave the best performance overall and are the most promising for the application in automated machine condition monitoring and expert free failure analysis. Further improvement can be achieved by performing a step-wise dimension reduction by first reducing the features using the Principal Component Analysis method, then further reduction with the Non-Linear Fisher technique.

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Section: Classifiers Using Pca and Kpca Methodsmentioning

confidence: 99%

Evaluation of methods for reduction of surface texture features

Stachowiak

Podsiadło

2006

Tribol Lett

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“…Hence, methods that give independent components are more powerful than those who give uncorrelated components. For instance, principal component analysis (PCA) [4] based on eigenvalue decomposition is a famous method that gives uncorrelated components. But those components are usually not similar enough to source signals, especially for correlated signals.…”

Section: Introductionmentioning

confidence: 99%

High-resolution DOA estimation based on independent noise component for correlated signal sources

Chen

Jen

Chang

2008

Neural Comput & Applic

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In this article, a modified complex-valued FastICA algorithm is utilized to extract the specific feature of the Gaussian noise component from mixtures so that the estimated component is as independent as possible to the other non-Gaussian signal components. Once the noise basis vector is obtained, we can estimate direction of arrival by searching the array manifold for direction vectors, which are as orthogonal as possible to the estimated noise basis vector especially for highly correlated signals with closely spaced direction. Superior resolution capabilities achieved with the proposed method in comparison with the conventional multiple signal classification (MUSIC) method, the spatial smoothing MUSIC method, and the signal subspace scaled MUSIC method are shown by simulation results.

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“…The processing of vectors in such a space is usually a difficult task due the ''dimensionality curse'' problem. In the last few years, a variety of dimensionality reduction transforms have been applied in the context of face recognition: Karhunen-Loève (KL) transform (Turk and Pentland, 1991) (also known as Principal Component Analysis), Independent Component Analysis (Bartlett et al, 2002;Fortuna and Capson, 2004), and Discriminant Analysis (LDA) (also called Fisher Discriminant Analysis) (Zhao et al, 1998). Some extensions of Principal Component Analysis have been studied and applied to face recognition: Nonlinear Principal Component Analysis (Krame, 1991), Kernel Principal Component Analysis (Yang et al, 2000).…”

Section: Introductionmentioning

confidence: 99%