The mean shift (MS) algorithm is an iterative method introduced for locating modes of a probability density function. Although the MS algorithm has been widely used in many applications, the convergence of the algorithm has not yet been proven. In this study, the authors modify the MS algorithm in order to guarantee its convergence. The authors prove that the generated sequence using the proposed modified algorithm is a convergent sequence and the density estimate values along the generated sequence are monotonically increasing and convergent. In contrast to the MS algorithm, the proposed modified version does not require setting a stopping criterion a priori; instead, it guarantees the convergence after a finite number of iterations. The proposed modified version defines an upper bound for the number of iterations which is missing in the MS algorithm. The authors also present the matrix form of the proposed algorithm and show that, in contrast to the MS algorithm, the weight matrix is required to be computed once in the first iteration. The performance of the proposed modified version is compared with the MS algorithm and it was shown through the simulations that the proposed version can be used successfully to estimate cluster centres.

Recent work has demonstrated the feasibility of extracting semantic categories directly from cortical measures (e.g., electroencephalography, EEG) during receptive tasks. Here, we automatically classify speech stimuli as either synonymous or non-synonymous with a prior prime in a speech-receptive task given only EEG data with up to 86.84% accuracy. An analysis of variance reveals no significant difference among support vector machine and k-nearest neighbours classifiers, but a significant effect of the individual subject on accuracy. To perform classification, we reduce the highly-parameterized space by three successive techniques: a ranking based on t-test similarity, another based on principal components analysis (PCA), and a third on linear discriminant analysis.

Principal curves are a non‐linear generalisation of principal components. They are smooth curves that pass through the middle of a data set to provide a new representation of those data to make tasks, such as visualisation and dimensionality reduction easier and more accurate. The subspace constrained mean shift (SCMS) algorithm is a recently proposed technique to find principal curves. The algorithm assumes that the complete data set is available in advance and that new data points cannot be added to the data set during the process. The algorithm finds the points on the principal curves by using the complete data set. In this paper, the authors investigate the situation where the entire data set is not available in advance and instead are sampled sequentially. They propose an incremental version of the SCMS algorithm that trains using a sequence of observations. Simulation results show the effectiveness of the proposed algorithm to find a principal curve using a stream of observations.

The problem of designing an optimal vector quantizer when there is access to the noise-free source has been well studied over the past five decades. However, in many real-world situations, the source output may be corrupted by some additive noise. In this case, we only have access to a noisy version of the data, but we expect a designed quantizer to minimize the distortion with respect to the clean (unavailable) data. It can be shown that the mean square distortion for an optimal noisy source vector quantization system can be decomposed into an optimum estimator, followed by an optimum source coder operating on the estimator output. We summarize this result first and then propose to use the kernel regression technique for estimating the clean data from the noisy version. The output of the kernel regression, as an estimate of the clean data, is quantized using the LBG vector quantizer. The proposed structure requires two sets of training data. The first set is used to train the kernel regression estimator. The second set is fed into the trained kernel regression system whose output is used to train the LBG vector quantizer. We show the effectiveness of the proposed structure through simulations with different numbers of code words.

Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of magnetic resonance (MR) images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have been introduced two new parameters in order to improve performance of traditional FCM in the case of noisy images. New parameters are computed using artificial neural networks and through an optimization problem, where need complex and time consuming computations. In this paper, we present a new method for efficient computation of these two parameters. We used genetic algorithm (GA) optimization method and showed capability of GA for finding optimal values of these parameters. Simplification of computation is advantage of new proposed method. Simulation results using noisy MR images, demonstrated effectiveness of proposed optimization method for noisy MR image segmentation. 1. Introduction