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Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop...
Similarity measure is a central problem in time series data mining. Although most approaches to this problem have been developed, with the rapid growth of the amount of data, we believe there is a challenging demand for supporting similarity measure in a fast and accurate way. In this paper, we propose a new time series representation model and a corresponding similarity measure, which is able to...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
This paper is concerned with the problem of optimal state estimation for multi-delay wireless network systems with nonzero-mean processes and measurement deviation under multiple packet dropouts. The orthogonal projection principle and reorganized innovation analysis approach are used to address the proposed problem. m + 1 Riccati equations of same dimension are given to solve the Kalman filter parameters...
In this paper, we propose a positioning system using a time-synchronized wireless network that can achieve high positioning accuracy without being dependent on any global positioning system (GPS) devices with cm-level accuracy as the main goal and objective. The proposed system utilizes time of arrival (ToA) method to estimate the target's position. Based on the results, we can achieve time synchronization...
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
In the field of network analysis, centrality values of graph nodes, which represents the importance of nodes, have been widely studied. In this paper, we focus on one of the most basic centrality measures: closeness centrality. Since the exact computation of closeness centrality for all nodes of a network is prohibitively costly for massive networks, algorithms for estimating closeness centrality...
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an...
Compressive spectral clustering (CSC) efficiently leverages graph filter and random sampling techniques to speed up clustering process. However, we find that CSC algorithm suffers from two main problems: i) The direct use of the dichotomy and eigencount techniques for estimating laplacian matrix’s k-th eigenvalue is expensive. ii) The computation of polynomial approximation repeats in each iteration...
The number of triangles in a graph is useful to deduce a plethora of important features of the network that the graph is modeling. However, finding the exact value of this number is computationally expensive. Hence, a number of approximation algorithms based on random sampling of edges, or wedges (adjacent edge pairs) have been proposed for estimating this value. We argue that for large sparse graphs...
In the big data era, the information about the same object collected from multiple sources is inevitably conflicting. The task of identifying true information (i.e., the truths) among conflicting data is referred to as truth discovery, which incorporates the estimation of source reliability degrees into the aggregation of multi-source data. However, in many real-world applications, large-scale data...
Obesity is increasing globally and is a risk factor for many chronic conditions such as such as heart disease, sleep apnea, type-2 diabetes, and some cancers. Research shows that food logging is beneficial in promoting weight loss. Crowdsourcing has also been used in promoting dietary feedback for food logging. This work investigates the feasibility of crowdsourcing to provide support in accurately...
One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study...
Increasing environmental pollutions, lack of power in remote places and demand for more energy makes us to seek new energy sources. Wind and solar hybrid energy have being popular ones owing to abundant, complement nature, ease of availability and convertibility to the electric energy. For hybridizing solar-wind system DC-AC or separate DC-DC converters are used one for each source. They will be connected...
In general cases, particularly in developing countries, the weakness of the national electric grid is due to overloading, high temperatures and bad weather. In such systems, the interruptions of electricity supply can take several hours to days. To prevent the power deficit, consumers can use diesel generators that are very noisy and highly polluting. But, as alternative, this paper proposes a combination...
In given paper offered methods and algorithms of determination of complexity of test questions for formation a database system of the adaptive test control for objective estimation of knowledge of students (pupils) in the course of training learning systems.
This paper presents a method for estimating object poses based on force sensor information using a particle filter. Autonomous robots often face uncertainty of measurement of object position when they manipulate objects. Related studies dealing with uncertainty in manipulation mainly focused on motions while grasping objects. In contrast, however, a non-grasp contact motion can be advantageous because...
In monocular vision systems, lack of knowledge about metric distances caused by the inherent scale ambiguity can be a strong limitation for some applications. We offer a method for fusing inertial measurements with monocular odometry or tracking to estimate metric distances in inertial-monocular systems and to increase the rate of pose estimates. As we performed the fusion in a loosely-coupled manner,...
Lane estimation plays a central role for Driver Assistance Systems, therefore many approaches have been proposed to measure its performance. However, no commonly agreed metric exists. In this work, we first present a detailed survey of the current measures. Most of them apply pixel-level benchmarks on camera images and require a time-consuming and fault-prone labeling process. Moreover, these metrics...
Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There are today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using...
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