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This paper discusses the problem of kernel selection in Reproducing Kernel Hilbert Spaces (RKHS) for nonlinear system identification and the use of a derivative norm regularization, in place of the traditional functional norm regularization. However in the proposed formulation, an optimal representer for the estimated function cannot be defined. Here, this problem is investigated and a representer...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
Environmental sensors monitor supercomputing facility health, generating massive data in the largest facilities. Current state-of-the-art is for human operators to evaluate environmental data by hand. This approach will not be viable on Exascale machines, nor is it ideal on current systems. We evaluate effectiveness of the DBSCAN algorithm for identifying anomalies in supercomputing sensor data. We...
This paper addresses the problem of automatic machine analysis based severity scoring of psoriasis skin disease. Three different disease parameters namely, erythema, scaling and induration are considered for such severity grading. Given an image containing a psoriatic plaque the task is to predict severity scores for all the three parameters. This paper presents a novel deep CNN based architecture...
Intelligent Transportation System (ITS) uses traffic data gathered by crowdsensing technology, which can easily get vast amounts of data from ordinary people's mobile devices, to ease congestion. However, crowdsensing also highlights the problem that the abnormal data, which we often call as outliers, may be collected for analyzing and then decrease the performances of ITS. To deal with this problem,...
This paper presents a fast deblurring algorithm to remove camera motion blur from a single photograph using built-in gyroscopes and strong edge prediction. An inaccurate blur kernel or point spread function (PSF) usually leads to an unsatisfying restored result. Hence, we propose a robust three-phase method for accurate PSF estimation. In the first stage, we utilize the embedded gyroscopes to compute...
Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle...
In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. Traditional spectrum sensing assumes Gaussian noise (or of other given distributions) in general. However when secondary users (SUs) have no prior information about the measurement distributions, the spectrum sensing schemes assuming given distribution...
Data-driven solutions to Electric Vehicle (EV) range estimation is attracting attention recently due to the prevalence of Internet of Things (IoT). However, there raise the Big Data problems with the increased volume and number of sensory sources of unstructured data collected from the EV equipped with In-Vehicle Networks. This means that traditional statistical analysis and Machine Learning tools...
Nearly all existing dimension reduction methods on 2D matrix-valued image predictors are unsupervised or supervised without preserving matrix structure, which can result in loss of the structure-specific relation between the response and predictors. In this paper, we propose a kernel-based solution for supervised dimension reduction which preserves the matrix structure of the reduced predictors. This...
How to make a decision is a critical problem in speaker verification system and it directly affects the final verification results. This paper describes a new efficient threshold setting method that performs speaker verification system with I-vector technique. Unlike typical system under laboratory, it is difficult to obtain a large number of data to fully estimate the speaker recognition threshold...
Traditionally, investors try to estimate short term portfolio volatility based on daily return. When tick-by-tick data are available, investors use different volatility estimators based on high-frequency data to evaluate the portfolio risk in the hope of outperforming those based on low-frequency data. In this paper, we optimize block realized kernel estimator in Hautsch et al. (2015) and propose...
Limited access to supervised information may forge scenarios in real-world data mining applications, where training and test data are interconnected by a covariate shift, i.e., having equal class conditional distribution with unequal covariate distribution. Traditional data mining techniques assume that both training and test data represent an identical distribution, therefore suffer in presence of...
Kernel density estimation is a popular method for identifying crime hotspots for the purpose of data-driven policing. However, computing a kernel density estimate is computationally intensive for large crime datasets, and the quality of the resulting estimate depends heavily on parameters that are difficult to set manually. Inspired by methods from image processing, we propose a novel way for performing...
This paper proposes a novel non-parametric method to robustly embed conditional and posterior distributions to reproducing Kernel Hilbert space (RKHS). Robust embedding is obtained by the eigenvalue decomposition in the RKHS. By retaining only the leading eigenvectors, the noise in data is methodically disregarded. The non-parametric conditional and posterior distribution embedding obtained by our...
Computational complexity of the local stereo matching method is affected by disparity ranges. In case of depth estimation in sequential stereo images, high computational complexity is a problem in terms of real-time processing. In this paper, we propose a temporal correlation based stereo matching method in sequential images. Using temporal information in a sequential stereo matching method provides...
Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR)...
To be applicable to real world problems, much reinforcement learning (RL) research has focused on continuous state spaces with function approximations. Some problems also require continuous actions, but searching for good actions in a continuous action space is problematic. This paper suggests a novel relevance vector sampling approach to action search in an RL framework with relevance vector machines...
In this paper, we study the effects of using smoothed variance estimates in place of the sample variances on the performance of stochastic kriging (SK). Different variance estimation methods are investigated and it is shown through numerical examples that such a replacement leads to improved predictive performance of SK. An SK-based dual metamodeling approach is further proposed to obtain an efficient...
One of the central problems in machine learning and pattern recognition is how to deal with high-dimensional data either for visualization or for classification and clustering. Most of dimensionality reduction technologies, designed to cope with the curse of dimensionality, are based on Euclidean distance metric. In this work, we propose an unsupervised nonlinear dimensionality reduction method which...
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