The Power-Oja method for decentralized subspace estimation/tracking
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This work proposes a decentralized and adaptive subspace estimation method, called the Power-Oja (P-Oja) method. Existing decentralized subspace tracking algorithms have slow convergence rate or are unable to adapt to time varying statistics. To resolve these issues, the P-Oja method is developed by combining the power method with Oja's learning rule. Our key innovation lies on the design of a modified objective function with enhanced spectral gap property. This allows the P-Oja method to track the principal subspace more quickly with a finite number of samples. Interestingly, the resulting method coincides with the conventional Oja's learning rule in some special cases. To enable decentralized signal processing, we further demonstrate that the proposed method can be implemented by using a gossip algorithm. Our simulation results show that the proposed P-Oja outperforms the conventional Oja's method in terms of estimation accuracy, and the power method in terms of tracking performance. The effect of the communication graph on the tracking performance is also studied.Keywords:
Tracking (education)
Active appearance models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to find the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can find the optimal AAM subspace model rapidly and improve the performance of AAM significantly.
Active appearance model
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The purpose of this paper is to estimate the interference subspace quickly. We show that the solution of the proposed constrained optimization problem results the signal subspace. Then a novel subspace tracking algorithm is propounded. Theoretical analysis and simulation results show that the interference subspace could be tracked quickly by utilized this algorithm.
Signal subspace
Tracking (education)
Algorithm design
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The thesis introduce a solution of using DataGrid coned and ComboBox control to realize DataBase of querying
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An evacuation model based on subspace is established.Personnel moves from a subspace with low degree to the higher one,and by this means,personnel in different subspace can determine his whole evacuation route.An evacuation in a building with multi-room is simulated.The result of simulation shows that the subspace model is an effective means in evacuation simulation.
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This paper presents a fast algorithm for robust subspace recovery. The datasets considered include points drawn around a low-dimensional subspace of a higher dimensional ambient space, and a possibly large portion of points that do not lie nearby this subspace. The proposed algorithm, which we refer to as Fast Median Subspace (FMS), is designed to robustly determine the underlying subspace of such datasets, while having lower computational complexity than existing methods. Numerical experiments on synthetic and real data demonstrate its competitive speed and accuracy.
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A new source-independent subspace technique for adaptive array processing applications incorporates sidelobe control in the subspace selection process. The subspace is determined solely by the desired mainbeam width and a priori knowledge of the array manifold, and is therefore independent of the directional interference environment. This technique achieves significant computational savings since a data-dependent subspace does not have to computed. Due to the subspace construction, the algorithm provides enhanced sidelobe control while still achieving nulling performance comparable with other subspace techniques.< >
Manifold (fluid mechanics)
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PCA-subspace method has been proposed for network-wide anomaly detection. Normal subspace contamination is still a great challenge for PCA although some methods are proposed to reduce the contamination. In this paper, we apply PCA-subspace method to six-month Origin-Destination (OD) flow data from the Abilene. The result shows that normal subspace contamination is mainly caused by anomalies from a few strongest OD flows, and seems unavoidable for subspace method. Further comparison of anomalies detected by subspace method and manually tagged anomalies from each OD flows, we find that anomalies detected by subspace method are mainly caused by anomalies from medium and a few large OD flows, and most anomalies of minor OD flows are buried in abnormal subspace and hard to be detected by PCA-subspace method. We analyze the reason for those anomalies undetected by subspace method and suggest to use normal subspace to detect anomalies caused by a few strongest OD flows, and to further divide abnormal subspace to detect more anomalies from minor OD flows. The goal of this paper is to address limitations neglected by prior works and further improve the subspace method on one hand, also call for novel detection methods for network-wide traffic on another hand.
Anomaly (physics)
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The subspace method proposed by Watanabe offers the basic concept of subspace construction, but the issue of how to use the limited samples to construct effective subspace to avoid the problem of the subspace inclining toward mean vectors remains unresolved. To cope with this problem, the authors have proposed the combination method (CM), which constructs the subspace from several groups including different number of samples divided from the whole training samples. The CM obtained a high recognition rate of 97.76% with respect to ETL9B, the largest database of hand-written characters in Japan. Next, the issues of how to improve the recognition accuracy and how to accelerate the recognition speed are dealt with. In this paper, we propose a new method called the uniform division method (UDM), which uses the uniformly divided training samples to construct a subspace. Compared to the CM given earlier, the UDM is very simple and effective enough to improve the accuracy of recognition. The UDM algorithm and the experiments with ETL9B are described.
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This paper analyzed the DataGrid Web Server Control. DataGrid is one of the most popular control of ASP .NET which is used to render data to a Web page in tabular form. This paper provides two types of typical usage of DataGrid.
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