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Today's applications deal with multiple types of information: graph data to represent the relations between objects and attribute data to characterize single objects. Analyzing both data sources simultaneously can increase the quality of mining methods. Recently, combined clustering approaches were introduced, which detect densely connected node sets within one large graph that also show high similarity...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
An approach of ant colony optimization combing gradient and relative difference of statistical means to image edge detection is proposed in this paper. The values of gradient and the relative difference of statistical means are extracted for the ants' searching. Experimental results show that the superior performances of the proposed algorithm.
Context extraction for local fusion (CELF) is a local approach that combines multiple classifier outputs with the help of feature space information. CELF is based on an objective function that integrates context extraction and decision fusion. Context extraction divides the feature space into homogeneous regions; decision fusion combines multiple classifier outputs in each region or context. Although...
We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. Our formulation is based on a softer version of robust optimization called comprehensive robustness. We show that this formulation is equivalent to regularization by any arbitrary convex regularizer. We explain how the connection...
We propose a new distributed algorithm for sparse variants of the network alignment problem, which occurs in a variety of data mining areas including systems biology, database matching, and computer vision. Our algorithm uses a belief propagation heuristic and provides near optimal solutions for this NP-hard combinatorial optimization problem. We show that our algorithm is faster and outperforms or...
Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally...
The performance optimization of many man-made systems belong to simulation-based constrained optimization (SBCO), where the evaluation of both the performance and the constraint have no closed form expression and are based on simulation. The simulation-based estimate of both the performance and the feasibility are usually time-consuming and noisy. So it is of great practical interest to study how...
In this article a primal barrier interior-point method for moving horizon estimation (MHE) is presented. It exploits the structure of the KKT systems yielding an algorithm with linear complexity in the horizon length as opposed to cubically as in unstructured solvers. Ideas of square root covariance Kalman filtering are proposed in order to update covariance matrices occurring in the factorization...
In the first instalment of this paper, a Newton-like extremum-seeking (ES) scheme was developed for application to problems involving optimisation of plants for which the curvature of input-output relationship is operating condition dependent. Strong operating condition dependence of the plant map curvature can be seen, for example, when using a phaseshift controller to reduce the limit-cycle pressure...
For the multisensor linear discrete time-invariant systems with correlated measurement noises and with different measurement matrices, based on weighted least squares (WLS) method, applying orthogonal transformation, two weighted measurement fusion Kalman filtering algorithms are presented. Using information filter, it is proved that they are functionally equivalent to the centralized fusion Kalman...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
In this paper we propose a technique to determine the decision thresholds in multi-bit distributed detection. Detection thresholds are required to quantize the acquired information from the environment to send them to a fusion center. In multi-bit detection, decision making is complicated and in most cases, methods based on simulation or Person by Person Optimizations are applied to find the thresholds...
This paper discusses the robust fault detection design for the criteria such as ??-/????,??2/???? and????/???? in which Gd, the transfer function from disturbance to the measurement output, is a tall transfer matrix and Dd is not full column rank. It is shown that the faults in a subspace can be made arbitrarily sensitive, while the faults in the complementary subspace have bounded sensitivities that...
This paper describes a noise-aware dominance operator for evolutionary algorithms to solve the multiobjective optimization problems (MOPs) that contain noise in their objective functions. This operator takes objective value samples of given two individuals (or solution candidates), estimates the impacts of noise on the samples and determines whether it is confident enough to judge which one is superior/inferior...
Many medical image segmentation techniques have been proposed by lots of authors but they are mainly dedicated to particular solutions. There is no generic method for solving the image segmentation problem. The difficulty comes from that two types of noise are presented in medical images: physical noise due to the acquisition system, for example, Optical, X-rays and MRI, and physiological noise due...
Textural coarseness for textural feature are compared. The problem addressed is to determine which texture feature optimize retrieval rate. Many textural features have been proposed in different papers. No much focused on comparative textural coarseness study has appeared. The goal is compared and evaluating in a quantitative manner three types of textural coarseness, namely gray level co-occurrence...
This paper proposes a Reinforcement Self-Organizing Interval Type-2 Fuzzy System with Ant Colony Optimization (RSOIT2FS-ACO) method. The antecedent part in each fuzzy rule of the RSOIT2FS-ACO uses interval type-2 fuzzy sets in order to improve system robustness to noise. There are no fuzzy rules initially. The RSOIT2FS-ACO generates all rules online. The consequent part of each fuzzy rule is designed...
This paper presents a novel strategy of adaptive filtering which provides an automatic self-configuration of the filter structure in terms of memory length. By monitoring the adaptive mixing of a normalized combination of two competing filters with a different number of coefficients, an online estimate of the optimum filter length is obtained and used to dynamically scale the size of the employed...
This paper discusses the process improvements for resolving gate oxide integrity (GOI) issue using the Taguchi method through reliability engineering for eliminating shallow trench isolation (STI) edge failure mode. The selected process parameters are narrowed down to STI/ILD stress, silicide residue, nitride residue, and other surface contaminants. The analysis of S/N ratio show that the most GOI...
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