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Co-clustering is a promising technique for summarizing cooccurrence information such as purchase history transactions and document-keyword frequencies. A close connection between fuzzy c-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms, which are induced by the GMMs concept, were proposed. Multinomial mixture models (MMMs) is a probabilistic model...
Hyperspectral unmixing is a hot topic in signal and image processing. A high-dimensional data can be decomposed into two non-negative low-dimensional matrices by Non-negative matrix factorization(NMF). However, the algorithm has many local solutions because of the non-convexity of the objective function. Some algorithms solve this problem by adding auxiliary constraints, such as sparse. The sparse...
In this study, two clustering frameworks are proposed based on a maximizing model of spherical Bezdek-type fuzzy clustering are proposed. One using possibilistic c-means, and the other using multi-medoids. In each framework, the basic model and its kernelization are presented, along with an appropriate spectral clustering technique. Kernelization allows the frameworks to capture nonlinear-bordered...
Nonnegative matrix factorization (NMF) has been widely used to reduce dimensionality of data in image processing and various applications. Incorporating the geometric structure into NMF, graph regularized nonnegative matrix factorization (GNMF) has shown significant performance improvement in comparison to conventional NMF. However, both NMF and GNMF require the data matrix to reside in the memory,...
We consider the problem of verifying the identity of a distribution: Given the description of a distribution over a discrete support p = (p1, p2,, pn) how many samples (independent draws) must one obtain from an unknown distribution, q, to distinguish, with high probability, the case that p = q from the case that the total variation distance (L1 distance) ||p -- q|| 1≥ ε?...
A classical theorem of Spencer shows that any set system with n sets and n elements admits a coloring of discrepancy O(√n). Recent exciting work of Bansal, Lovett and Meka shows that such colorings can be found in polynomial time. In fact, the Lovett-Meka algorithm finds a half integral point in any "large enough" polytope. However, their algorithm crucially relies on the...
The principal roles of capacitors in power systems are to supply reactive power to minimize loss and to improve the voltage profile. The optimization problem has to find the appropriate placement of capacitors that lead both to minimal system power losses and minimal total capacitor costs. This problem is largely presented in literature and is commonly solved using heuristic optimization techniques...
This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP that decomposes into small independent subproblems. We test the decomposition algorithm using a simple power management...
Recently, attention has been paid to tracking methods using sparse representation. Assuming that the representation residuals follow Gaussian distribution, the multi-object tracking methods based on sparse representation are proposed. However, these methods are sensitive to outliers such as occlusion due to the assumption of Gaussian distribution. In our paper, a novel sparse representation based...
This paper proposes a new composition method to represent semantic compositionality of sentences. Using the unfolding recursive autoencoders, we build sentence representing trees from the original sentences of words. We utilize trained word embeddings and sentence parser to train the model, and we can build sentence representing trees from the trained model. We further propose to use dynamic average...
The classification with instances which can be tagged with any of the 2L possible subsets from the predefined L labels is called multi-label classification. Multi-label classification is commonly applied in domains, such as multimedia, text, web and biological data analysis. The main challenge lying in multi-label classification is the dilemma of optimising label correlations over exponentially large...
This paper provides a linear approach to compute the voltages at any node on a residential grid based on the house instantaneous load and the presence of charging Plug-In Hybrid Electric Vehicles (PHEV) on the grid (and the corresponding instantaneous consumption or injection). Based on this linear operation, the paper provides a detailed Linear Programming (LP) formulation of the problem of charging...
Attacking is one of the popular tactics usually chosen by a soccer coach. With attacking effectively, chances to score goals can be enhanced as many as possible. Better attack needs good ball passing, and that is the purpose and focus of this research. Previous robotic soccer simulations employed simple weighting method with simple criteria and does not produce optimal ball passing. To overcome this...
Recently, semi-supervised sparse feature selection, which can exploit the large number unlabeled data and small number labeled data simultaneously, has placed an important role in web image annotation. However, most of the semi-supervised feature selection methods are developed for single-view data, which can not reveal and leverage the correlated and complemental information between different views...
We consider the problem of optimizing a sum of local objective functions corresponding to multiple agents. We discuss a distributed model where the agents can only exchange quantization data over a time-varying network. For solving this problem, we propose a method that involves agents updating their states by weighted averaging and probabilistically quantized information. The method indicates how...
This paper discusses the use of large-dimension reconfigurable suspended cable-driven parallel robots (CDPR) to substitute for conventional gantry nacelles that carry workers in an airplane maintenance workshop. The reconfiguration of the CDPR is considered as a multi-objective optimization problem with two performance indices. One criterion is the sum of the cable tensions which is directly related...
This paper presents a new weight adaptation method for Adaptive Weighted Aggregation (AWA) that is a powerful multi-start framework of scalarized decent methods for multi-objective function optimization. AWA iteratively adapts weight vectors for a scalarized decent method in order to improve the coverage of an approximate solution set. AWA has been reported to show better performance than conventional...
Aiming at the velocity jump caused by joint failure of manipulator, a method is proposed to reduce velocity jumps in both Cartesian space and joint space. Based on the analysis of velocity jump, the reduction problem is turned into an optimization problem of finding the best compensation vector for joint velocity. The gradient of the reduced manipulability is applied to represent the weight for joint...
Agglutinative languages, such as Hungarian, use inflection to modify the meaning of words. Inflection is a string transformation which describe how can a word converted into its inflected form. The transformation can be described by a transformational string. The words can be classified by their transformational string, so inflection is considered as a classification. Linear separability of clusters...
An approach to design of electromechanical system control based on the speed bi-gradient method is considered. The design procedure is applied for a control system consisting of Lagrangian output subsystem and affine input subsystem. The simulation results show well-enough tracking of output subsystem trajectories to reference signal under parametric uncertainties.
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