Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

计量经济分析 = Econometric analysis

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

System Identification I

Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequentially, online detection method that can process streaming data is preferred. In this paper, we formulate a distributed online OCSVM for anomaly detection over networks and get a decentralized cost function. To get the decentralized implementation without transmitting the original data, we use a random approximate function to replace the kernel function. Furthermore, to find an appropriate approximate dimension, we add a sparse constraint into the decentralized cost function to get another one. Then we minimize these two cost functions by stochastic gradient descent and derive two distributed algorithms. Some theoretical analysis and experiments are performed to show the effectiveness of the proposed algorithms. Experimental results on both synthetic and real datasets reveal that both of the proposed algorithms achieve low misdetection rates and high true positive rates. Compared with other state-of-the-art anomaly detection methods, the proposed distributed algorithms not only show good anomaly detection performance, but also require relatively short running time and low CPU memory consumption.

Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

Distributed estimation over networks has received much attention in recent years due to its broad applicability. Many signals in nature present high level of sparsity, which contain only a few large coefficients among many negligible ones. In this paper, we address the problem of in-network distributed estimation for sparse vectors, and develop several distributed sparse recursive least-squares (RLS) algorithms. The proposed algorithms are based on the maximum likelihood framework, and the expectation-maximization algorithm, with the aid of thresholding operators, is used to numerically solve the sparse estimation problem. To improve the estimation performance, the thresholding operators related to l0- and l1-norms with real-time self-adjustable thresholds are derived. With these thresholding operators, we can exploit the underlying sparsity to implement the distributed estimation with low computational complexity and information exchange amount among neighbors. The sparsity-promoting intensity is also adaptively adjusted so that a good performance of the sparse solution can be achieved. Both theoretical analysis and numerical simulations are presented to show the effectiveness of the proposed algorithms.

Distributed Sparse Recursive Least-Squares Over Networks

Distributed estimation over networks has received a lot of attention due to its broad applicability. In diffusion type of distributed estimation, the parameters of interest can be well estimated from noisy measurements through diffusion cooperation between nodes. Meanwhile, the consumption of communication resources is low, since each node exchanges information only with its neighbors. In previous studies, most of the cost functions used in diffusion distributed estimation are based on mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not always hold in real-world environments. In non-Gaussian cases, the information theoretic learning (ITL) provides a more general framework and has a better performance than the MSE-based method. In this work, we incorporate information theoretic measure into the cost function of diffusion distributed estimation. Moreover, an information theoretic measure based adaptive diffusion strategy is proposed to further promote estimation performance. Simulation results show that the diffusion ITL-based distributed estimation method can achieve superior performance comparing to the standard diffusion least mean square (LMS) algorithm when the noise is modeled to be non-Gaussian.

Diffusion Information Theoretic Learning for Distributed Estimation Over Network

One-bit quantization can significantly reduce the massive multiple-input and multiple-output (MIMO) system hardware complexity, but at the same time it also brings great challenges to the system algorithm design. Specifically, it is difficult to recover information from the highly distorted samples as well as to obtain accurate channel estimation without increasing the number of pilots. In this paper, a novel inference algorithm called variational approximate message passing (VAMP) for one-bit quantized massive MIMO receiver is developed, which attempts to exploit the advantages of both the variational Bayesian inference algorithm and the bilinear generalized approximated message passing algorithm to accomplish joint channel estimation and data detection in a closed form with first-order complexity. Asymptotic state evolution analysis indicates the fast convergence rate of VAMP and also provides a lower bound for the data detection error. Moreover, through extensive simulations, we show that VAMP can achieve excellent detection performance with low pilot overhead in a wide range of scenarios.

One-Bit Quantized Massive MIMO Detection Based on Variational Approximate Message Passing

Cloud computing enables customers with limited computational resources to outsource their huge computation workloads to the cloud with massive computational power. However, in order to utilize this computing paradigm, it presents various challenges that need to be addressed, especially security. As eigen-decomposition (ED) and singular value decomposition (SVD) of a matrix are widely applied in engineering tasks, we are motivated to design secure, correct, and efficient protocols for outsourcing the ED and SVD of a matrix to a malicious cloud in this paper. In order to achieve security, we employ efficient privacy-preserving transformations to protect both the input and output privacy. In order to check the correctness of the result returned from the cloud, an efficient verification algorithm is employed. A computational complexity analysis shows that our protocols are highly efficient. We also introduce an outsourcing principle component analysis as an application of our two proposed protocols.

Chunguang Li

Papers

Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

Distributed Sparse Recursive Least-Squares Over Networks

Diffusion Information Theoretic Learning for Distributed Estimation Over Network

One-Bit Quantized Massive MIMO Detection Based on Variational Approximate Message Passing

Outsourcing Eigen-Decomposition and Singular Value Decomposition of Large Matrix to a Public Cloud