Kernel Pca

The retrieval of biophysical variables using canopy reflectance models is hindered by the fact that the inverse problem is ill posed. This is due to the measurement, model errors and the inadequacy between the model and reality, which produces similar reflectances for the ...

This paper presents a novel Daubechies-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. The palmprint is first transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, weighted Euclidean linear distance based NN classifier and support vector machine (SVM) are comparatively performed for similarity measurement. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.

The aim of this article is to present the potential of Kernel Principal Component Analysis (Kernel PCA) in the field of vision based robot localization. Using Kernel PCA we can extract features from the visual scene of a mobile robot. The analysis is applied only to local features so as to guarantee better computational performance as well as translation invariance.

Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessing step for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data. This is a characteristic common to all PCA-based techniques. In this paper, we propose a method which is able to remove outliers in data vectors and replace them with the estimated values via kernel PCA. By repeating this process several times, we can get the feature components less affected with outliers. We apply this method to a set of face image data and confirm its validity for a recognition task.

The cardiovascular diseases are one of the main causes of death around the world. Automatic detection and classification of electrocardiogram (ECG) signals are important for diagnosis of cardiac irregularities. This paper proposes to apply the Support Vector Machines (SVM) classification, to diagnose heartbeat abnormalities, after performing feature extraction on the ECG signals. The experiments were conducted on the ECG signals from the MIT-BIH arrhythmia database [1] to classify two different abnormalities and normal beats. Kernel Principal Component Analysis (KPCA) is used for feature extraction since it performes better than PCA on ECG signals due to their nonlinear structures. This is demonstrated in a previous work [2]. Two multi-SVM classification schemes are used, One-Against-One (OAO) and One-Against-All (OAA), to classify the ECG signals into different disease categories. The experiments conducted show that SVM combined with KPCA performs better than that without feature extraction. Moreover, our results show a better performance in Gaussian KPCA feature extraction with respect to other kernels. Furthermore,the performance of Gaussian OAA-SVM combined with KPCA has higher average accuracy than Gaussian OAA-SVM in ECG classification.

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classiffer. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.

Automated inspection of apple quality involves computer recognition of good apples and blemished apples based on geometric or statistical features derived from apple images. This paper introduces a Gabor feature-based kernel principal component analysis (PCA) method by combining Gabor wavelet representation of apple images and the kernel PCA method for apple quality inspection using near-infrared (NIR) imaging. First, Gabor wavelet decomposition of whole apple NIR images was employed to extract appropriate Gabor features. Then, the kernel PCA method with polynomial kernels was applied in the Gabor feature space to handle non-linear separable features. The results show the effectiveness of the Gabor-based kernel PCA method in terms of its absolute performance and comparative performance compared to the PCA, kernel PCA with polynomial kernels, Gabor-based PCA and the support vector machine methods. Using the proposed Gabor kernel PCA eliminated the need for local feature segmentation, but also resolved the non-linear separable problem. An overall 90.6% recognition rate was achieved.

This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal component analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) are applied to kinematic parameters and compared for feature extraction. LDA in combination with naive Bayes leads to an accuracy of 91% for person-dependent recognition of four discrete affective states based on observation of barely a single stride. Extra-success comparing to inter-individual recognition is twice as much. Furthermore, affective states which differ in arousal or dominance are better recognizable in walking. Though primary task of gait is locomotion, cues about a walker's affective state are recognizable with techniques from machine learning.

Automatic target recognition (ATR) system performance over various operating conditions is of great interest in military applications. The performance of ATR system depends on many factors, such as the characteristics of input data, feature extraction methods, and classification algorithms. Generally speaking, ATR performance evaluation can be performed either theoretically or empirically. The theoretical evaluation method requires reasonably accurate underlying models

We develop gain adaptation methods that improve convergence of the Kernel Hebbian Algorithm (KHA) for iterative kernel PCA (Kim et al., 2005). KHA has a scalar gain parameter which is either held constant or decreased according to a predetermined annealing schedule, leading to slow convergence. We accelerate it by incorporating the reciprocal of the current estimated eigenvalues as part of a gain vector. An additional normalization term then allows us to eliminate a tuning parameter in the annealing schedule. Finally we derive and apply stochastic meta-descent (SMD) gain vector adaptation (Schraudolph, 1999, 2002) in reproducing kernel Hilbert space to further speed up convergence. Experimental results on kernel PCA and spectral clustering of USPS digits, motion capture and image denoising, and image super-resolution tasks confirm that our methods converge substantially faster than conventional KHA. To demonstrate scalability, we perform kernel PCA on the entire MNIST dataset.

Kernel PCA, as a multivariate statistical process monitoring (MSPM) tool, is a powerful technique capable of coping with non linear relations between variables, thus outperforming classical linear techniques when non linearities are present in data. In real industrial chemical processes, multiple plant operating modes often lead to multiple nominal operation regions, and MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. The existence of multiple operation modes is often more frequent than clearly expressed strong non linear relations between the variables involved. Non linear relations do exist, but the small variability allowed in key variables during normal plant operation prevents these non linear relations from being expressed in the data. In this work, a fault detection tool based on Kernel PCA is tested in such multiple operation modes environments, with final objective of implementing the tool in a real industrial instal...

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both ), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.

Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Classic linear feature extraction approaches, such as PCA, LDA, and FLDA, only take the 2-order statistics among gait patterns into account, and are not sensitive to higher order statistics of data. Therefore, KPCA is used to extract higher order relations among gait patterns for future recognition. Fast Fourier Transform (FFT) is employed as a preprocessing step to achieve translation invariant on the gait patterns accumulated from silhouette sequences which are extracted from the subjects walk in different speed and/or different time. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.