A kernel-based subtractive clustering method

In this paper, some classical and modern spectral estimation algorithms are incorporated in the conventional spectral subtraction method for improving the performance of speech enhancement. There are three methods are proposed in this paper: the SSW method, the SSY method and the SSM method. Some typical environment noise signals are chosen from the common noise group. The noisy speech signals adopted in the experiments are the clean speech contaminated by the additive noise and the convolution noise at different signal-to-noise ratios respectively. The Itakura-Saito spectrum distance between the enhanced speech and the clean speech is served as the measurement of the quality of the speech enhancement system. Each type of the noisy speech signals will be enhanced by the conventional spectrum subtraction method and the three proposed methods respectively. Experimental results demonstrated the superior performance of the proposed methods over the conventional spectrum subtraction method. It is concluded that each of them is adept to manipulating different type of noises according to the corresponding characteristics.

A simple clustering method is proposed for extracting representative subsets from lengthy data sets. The main purpose of the extracted subset of data is to use it to build prediction models (of the form of approximating functional relationships) instead of using the entire large data set. Such smaller subsets of data are often required in exploratory analysis stages of studies that involve resource consuming investigations. A few recent studies have used a subtractive clustering method (SCM) for such data extraction, in the absence of clustering methods for function approximation. SCM, however, requires several parameters to be specified. This study proposes a clustering method, which requires only a single parameter to be specified, yet it is shown to be as effective as the SCM. A method to find suitable values for the parameter is also proposed. Due to having only a single parameter, using the proposed clustering method is shown to be orders of magnitudes more efficient than using SCM. The effectiveness of the proposed method is demonstrated on phase space prediction of three univariate time series and prediction of two multivariate data sets. Some drawbacks of SCM when applied for data extraction are identified, and the proposed method is shown to be a solution for them.

A fuzzy inference system for wind-wave modeling

Support vector machines combining subtractive clustering method are proposed in this paper. Subtractive clustering method is used to select a set of cluster centers which are the data samples themselves as the representation of original massive set of training data. The new training set then is used to construct support vector machines. Two benchmarks on two-class recognition and multi-class problem are tested, and the results show that the support vector machines based on subtractive clustering have better or equal classification accuracy and generalization ability with smaller set of training data and cost less optimization computation time than conventional support vector machines.

In this paper, a novel semi-supervised subtractive clustering method using pair wise constraints is proposed. Because the traditional subtractive clustering method cannot obtain the better clustering accuracies, some pair wise constraints regarded as supervised information are applied to aiding the process of the traditional subtractive clustering. Experimental results show that the new presented semi-supervised clustering method can improve the clustering performance significantly compared to the unsupervised subtractive clustering.

Spatial clustering has been identified as an important technique in data mining owing to its various applications. In the conventional spatial clustering methods, data points are clustered mainly according to their geographic attributes. In real applications, however, the obtained data points consist of not only geographic attributes but also non-geographic ones. In general, geographic attributes indicate the data locations and non-geographic attributes show the characteristics of data points. It is thus infeasible, by using conventional spatial clustering methods, to partition the geographic space such that similar data points are grouped together. In this paper, we propose an effective and efficient algorithm, named incremental clustering toward the Bound INformation of Geography and Optimization spaces, abbreviated as BINGO, to solve the problem. The proposed BINGO algorithm combines the information in both geographic and non-geographic attributes by constructing a summary structure and possesses incremental clustering capability by appropriately adjusting this structure. Furthermore, most parameters in algorithm BINGO are determined automatically so that it is easy to be applied to applications without resorting to extra knowledge. Experiments on synthetic are performed to validate the effectiveness and the efficiency of algorithm BINGO.

Finding a new drug which is both safe and efficient is an expensive and time-consuming endeavour. In particular, establishing the ‘dose-effect relationship’ – how beneficial a drug is at different dosages – can be challenging. Predicting this curve requires gathering experimental data by exposing and recording how cells respond to various levels of the drug. However, extreme values are often observed at low and high dosages, potentially introducing errors that are hard to correct in the prediction process. Yet, these extreme observations are sometimes genuine so researchers cannot just ignore them. To improve dose-effect estimation, Zhou, Liu, Fang et al. developed a new general-purpose approach. It uses advanced statistical modelling to account for extremes in lab data. This strategy outperformed other methods when dealing with these observations while also providing higher efficiency in data analysis with more uniform data in experiments. To facilitate implementation, Zhou, Liu, Fang et al. set up a user-friendly tool baptized ‘REAP’; this free online resource allows scientists without advanced statistical experience to harness the new approach and to perform dose-effect analysis more easily and accurately. This could boost research across many different disciplines that examine the effects of chemicals on cells.

Surface display techniques, including contour mapping, usually use an interpolant when data values are on a rectangular grid; but when values are irregularly located some form of assumed or estimated distance-weighting function is often used to estimate the influence of data points at other locations. If moving average (or some other) techniques are then applied, slopes may be zero at data points; extrema may fall at data points exclusively; or surfaces may not pass through data points.An alternative method is proposed whereby a map is automatically divided into suitable triangular domains with a data point at each vertex. Surface estimation and contour plotting are then performed independently for each triangle using a measured or best-fit plane associated with each data point. The approach requires the utilization of three non-original aspects: 1) the derivation of a local homogeneous area coordinate system for any arbitrary triangle; 2) the construction of a data-structure linking each triangular domain with its three neighbouring triangles and three associated data points, and 3) the use of a conforming triangular finite-element interpolating function.Use of the first two of these concepts permits the economic generation and optimization of a triangular mesh from a set of data points. Optimization criteria used to define the best triangular partition are described in some detail, along with computer timing for this step. Use of the first and third concepts permits the interpolation of a smooth surface over the whole map even though each triangular element is estimated and plotted independently. The requirements for a suitable interpolating function are discussed, and an interpolant is suggested that preserves elevation and slope at each data point as well as elevation and slope continuity between domains. Extrema need not be located at data points but are constrained by their associated planes. Contour line segments are produced by division of each triangle into N2 sub-triangles, elevation estimation at each resulting node, and linear interpolation and plotting withing each sub-triangle. Resolution is then a function of N and local data point density. Extension of the method to higher dimensions is briefly discussed.

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Density weighted support vector data description

All data presented pertain to 11 advertised brands of same low-priced, high-consumption food product. The data span an eight-year period with nine data points for each brand. Two measurements were taken during first year with about a four-month interval between readings; remaining seven measurements were obtained at approximate yearly intervals. The 11 brands came from three separate markets, and each reported value was based on no fewer than 200 principal female shoppers in a family who used product category. Sampling was of two types: (1) replicated probability samples of blocks with clusters split proportionally between central city and surrounding suburban towns or (2) random samples from metropolitan telephone directory with specification of individual telephone numbers. The share-of-families data were obtained from question: of do you buy? If respondents had difficulty with usually buy, interviewers were instructed to add: the you buy most often. Share of families, for study's food category, is judged to be a reasonable estimate of market share based on an additional analysis. The surveys also included a question on units purchased per week. These data, in recent surveys, have been multiplied by users to develop individual volume shares. The resulting percentages have been close to share-offamily percentages. The reliability of brand bought as an indicator of share of families was also examined. Data from two additional questions were analyzed: What of do you have in house right now? and particular have you purchased most often in past three weeks? Although there are interesting differences, high similarity in percentages exists across three questions. The top-of-mind awareness data were obtained by asking, When you think of (the type of product) what first comes to mind? This was first question asked a respondent, after she was qualified as principal female shopper and a user of pro uct category. The top-of-mind awareness and share-of-families data for 11 brands are shown in figure. All data points are on a percentage basis. The order is based on market performance accumulated over nine data points. For each change in share-of-families between successive data points was determined algebraically by subtracting share of families for each data point from preceding data point. These values were accumulated and 11 brands were rank-ordered by algebraic total as shown in Column 1 of Tables 1 and 2. Thus, Brand A has largest total data point increase in share of families, and Brand K has largest total decrease in data point share changes.

Evaluation of the performance of clustering algorithms in kernel-induced feature space

The objective of the study summarized in this paper was to establish a robust method to detect outliers in rate and pressure data used for decline analysis and forecasting, pressure and rate transient analysis, and similar workflows. Presence of outliers contributes significantly to the uncertainty and non-uniqueness associated with the rate/pressure transient analysis workflow. In time-series data usually encountered in rate/pressure transient analysis workflows, we observed that the neighboring data points of an inlying data point will be closer to the point as compared to closeness of an outlier data point. The outlier detection method we developed exploits this observation. It relies on the concept of local density around a data point, and locality is quantified by a function of distance of the data point from its k-nearest neighbors. This concept further maps into a local outlier factor, which signifies the quantitative extent of a data point being an outlier. Outliers will have a higher value of local outlier factor than will inlying data points. A threshold value of local outlier factor is used to label the data points as outliers, and this threshold is set using the histogram of local outlier factor values. Most commonly used outlier detection methods in the industry assume that rate/pressure data follow a known model (e.g., a Gaussian probability distribution model), but outliers deviate strongly from this model. In the usual parametric-model-based approach, an a priori assumption of the correct model needs to be verified, which can limit the validity of the approach. The newly proposed outlier detection method, being non-parametric, is more robust as it is independent of the assumption that data follows a known model. We present a synthetic case study demonstrating the importance of outlier detection and outlining the limitations of currently used outlier detection methods. We then validate our method through synthetic examples generated using numerical models of multi-stage hydraulically fractured wells in unconventional reservoirs. Upon validation we demonstrate application of our method using field examples from four major shale plays.

Similarity search plays an important role in many applications involving high-dimensional data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information. In RHLM, for each individual data point in the training dataset, a local hashing model is learned and used to predict the hash codes of its neighboring data points. The local models from all the data points are globally aligned so that an optimal hash code can be assigned to each data point. After obtaining the hash codes of all the training data points, we design a robust method by employing l2,1 -norm minimization on the loss function to learn effective hash functions, which are then used to map each database point into its hash code. Given a query data point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same subject with different facial expressions, the algorithm segments the data set into groups corresponding to different facial expressions. Each facial image can be regarded as a point in a high-dimensional space, and the collection of images of the same subject resides on a manifold within this space. We show that different facial expressions reside on distinct subspaces if the manifold is unfolded. In particular, semi-definite embedding is used to reduce the dimensionality and unfold the manifold of facial images. Next, generalized principal component analysis is used to fit a series of subspaces to the data points and associate each data point to a subspace. Data points that belong to the same subspace are shown to belong to the same facial expression.