A novel model for Fisher discriminant analysis is developed in this paper. In the new model, maximal Fisher criterion values of discriminant vectors and minimal statistical correlation between feature components extracted by discriminant vectors are simultaneously required. Then the model is transformed into an extreme value problem, in the form of an evaluation function. Based on the evaluatio...

Fisher distance based Genetic Algorithm: GA clustering method which takes into account overlapped space among probability density functions of clusters in feature space is proposed. Through experiments with simulation data of 2D and 3D feature space generated by random number generator, it is found that clustering performance depends on overlapped space among probability density function of clu...

We present a method to generate probability distributions that correspond to metrics obeying partial differential equations generated by extremizing a functional J [g(mu nu) (theta(i)) ] , where g(mu nu) (theta(i)) is the Fisher metric. We postulate that this functional of the dynamical variable g(mu nu) (theta(i)) is stationary with respect to small variations of these variables. Our approach ...

t − n 2 . These convergent rates are optimal in the sense with L-initial perturbation. The adopted approach is the weighted energy method combining Fourier transform. It is also realized that, the effect of time-delay essentially causes the decay rate of the solution slowly down. These results significantly generalize and develop the existing study [37] for 1-D time-delayed Fisher-KPP type reac...

In this paper, we propose a new feature extraction method based on higher-order local auto-correlation (HLAC) and Fisher weight map (FWM). Widely used MFCC features lack temporal dynamics. To solve this problem, 35 types of local auto-correlation features are computed within two-dimensional local regions. These local features are accumulated over more global regions by weighting high scores on ...

Mika et al. [1] apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark datasets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O(l3) operations rather than the O(l4) of a näıve impl...

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is ;rst performed and then LDA is used for a second feature extraction in the KPCA-transformed ...

Maddala and Wu (1999) proposed a simple panel unit test using a Fisher type test. We propose using Percival and Walden (2006) approximate maximum likelihood estimator of the fractional integration order with a Fisher type test to create a simple panel fractional integration order test. By combining the estimator and the test with a wavestrap method to obtain critical values, we allow for cross-...