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Sparse non-negative matrix factorization (sNMF) allows for the decomposition of a given data set into a mixing matrix and a feature data set, which are both non-negative and fulfill certain sparsity conditions. In this paper it is shown that the employed projection step proposed by Hoyer has a unique solution, and that it indeed finds this solution. Then indeterminacies of the sNMF model are identified...
The parameters estimation of mixture distributions is an important task in statistical signal processing, Pattern recognition, blind equalization and other modern statistical tasks often call for mixture estimation. This paper aims to provide a realistic distribution based on Mixture of Generalized Gaussian distribution (MGG), which has the advantage to characterize the variability of shape parameter...
The Radon transform is a powerful method that has been used to filter coherent noise from seismic records and to reconstruct seismic data. In addition, it has a long history in image processing as a tool for feature extraction. An important shortcoming in exploration seismology, however, is the requirement of simple integration paths that often do not match well enough the spatio-temporal structure...
Volatility of the stock price is the key to the pricing problem of stock related derivatives in finance. Volatility appears in the diffusion term of the usual modeling of stock prices. One popular approach is to take volatility to be stochastic, and assumes that it satisfies a stochastic differential equation. Taking the stock price to be the observation, we may then pose the filtering problem of...
In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative models through Bayes's rule. Sometimes, however, it is convenient to express the models as probabilistic models, since they are generative in nature and can handle the representation...
Dynamic modeling of a steam generation system is necessary for analyzing the power plant and for control design studies. The paper presents a nonlinear dynamic simulation model based on the physical properties of water and steam with their different mass and energy balance equations. The developed dynamic model was used to build up a drum boiler simulator for the typical Romanian steam boilers from...
A new model has been established in order to represent one-phase water flow transients in pipeline systems. This model is based on a system of hyperbolic partial differential equations solved via the orthogonal collocation method. An observer can be designed from this model in order to estimate the unsteady friction when a transient event occurs in the pipeline. The Extended Kalman filter is chosen...
In this paper we extend the agglomerative hierarchical kernel spectral clustering (AH-KSC [1]) technique from networks to datasets and images. The kernel spectral clustering (KSC) technique builds a clustering model in a primal-dual optimization framework. The dual solution leads to an eigen-decomposition. The clustering model consists of kernel evaluations, projections onto the eigenvectors and a...
The deployment of the components of distributed systems is now often very dynamic - server-side components are virtualised so they can be dynamically deployed on a range of platforms including public and private clouds, while users expect to be able to install clients on devices from phones to tablets. This can introduce security problems that place data at risk. This paper describes a new method...
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion. Each observation is a random variable whose distribution is parameterized by the...
This paper highlights the importance of measuring systemic risk of commercial banks. Conditional Value-at-Risk (CoVaR) is used to measure the degree of "risk externalities" that a specific bank contributes to the whole banking system. Our analysis not only presents current levels of systemic risk of individual banks but also the changes with time passes. There is some evidence that larger...
Bayesian networks are powerful probabilistic models that have been applied to a variety of tasks. When applied to classification problems, Bayesian networks have shown competitive performance when compared to other state-of-the-art classifiers. However, structure learning of Bayesian networks has been shown to be NP-Hard. In this paper, we propose a novel approximation algorithm for learning Bayesian...
Response model is one of the most frequently used predictive model. So I make a comprehensive introduction about basic concepts, key functions and main contents of response model and evaluated the model. Through design for utility function and attribute weights to get market value function. In this progress, positive cases and negative cases must be considered. Calculating market value can help enterprises...
Negative and positive bias temperature instability (NBTI and PBTI) are described in the same model using the Reaction-Diffusion (RD) by taking into account all protagonist diffusion hydrogenate species; hydrogen atom (H), proton (H+) and hydrogen molecular (H2). This model is based on the probability that the passivated dangling bonds at the interface of silicon-oxide release the hydrogen H or proton...
Simulation of various manufacturing processes such as heat treatments is rapidly gaining importance in the industry for process optimization, enhancing efficiency and improving product quality. Case carburization followed by quenching is one such significant heat treatment process commonly used in the automotive industry. The equations to be solved for simulation of these processes are non-linear...
Data Caching on mobile clients is widely seen as an effective solution to increase data availability. A population is a “group of plant, group of people, and group of animal etc.” all is same species that live together and reproduce. Here a group of cached data items at clients in mobile ad hoc network called Group of Cached Data items (GCD). In this paper, the growth rates of cached data items at...
Data Sparsity incurs serious concern in collaborative filtering (CF). This issue is especially critical for newly launched CF applications where observed ratings are too scarce to learn a good model to predict missing values. There could be, however, information from other related domains which are with relatively denser data that can be utilized. This paper proposes a transfer-learning based approach...
Intrinsically high-dimensional data has recently been shown to exhibit substantial hubness in terms of skewness of the k-nearest neighbor occurrence frequency distribution. While some points arise as centers of influence and dominate most k-nearest neighbor sets, other points occur very rarely and barely affect the inferred models. Hubness has been shown to be highly detrimental to many learning tasks...
The paper compares Artificial Neural Network (ANN) model against traditional models in the modeling of population and external migration for Fiji population components during the years from 1986 to 2012. The performance of the various models used are based on the values of the various error functions such as the R-squared (R2), Root Square Mean Error (RSME), Mean Absolute Error (MAE), Standard Error...
Hidden Markov models (HMM) is a probabilistic model consisting of variables representing observations, variables that are hidden, the initial state distribution, transition matrix, and parameters for all observation distributions. The said model is commonly used in speech recognition field and it has seen an increase in terms of usage, which include user profiling in mobile communication networks,...
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