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Abstract The random matrix (RM) model is a typical extended object-modeling method that has been widely used in extended object tracking. However, existing RM-based filters usually assume that the measurements follow a Gaussian distribution, which may lead to a decrease in accuracy when the filter is applied to the lidar system. In this paper, a new observation model used to modify an RM smoother by considering the characteristics of 2D LiDAR data is proposed. Simulation results show that the proposed method achieves a better performance than the original RM tracker in a 2D lidar system.

Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years. In this paper, we propose a graph regularized constrained nonnegative matrix factorization with Lp Smoothing (GCNMFS) for image representation. Specifically, the main contributions of the proposed GCNMFS method include as follows: firstly, the geometric manifold structure hidden in data is effectively exploited by adopting a graph regularizer. Secondly, the label information of labeled samples is incorporated into the model of NMF without additional parameters. Finally, the Lp smoothness constraint is used to constrain the basis matrix, and thus a smooth and more accurate solution is produced. Moreover, an effective optimization scheme is presented to solve the proposed model. Extensive experiments on several image datasets show the proposed GCNMFS method can achieve better performance than other state-of-the-art methods in clustering.

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

Low-rank representation (LRR) has attracted wide attention of researchers in recent years due to its excellent performance in the exploration of high-dimensional subspace structures. However, in the existing semi-supervised learning problem based on the LRR method, graph construction and semi-supervised learning are two separate steps. Therefore, the existing label information in the data set is not well used to guide the construction of the affinity graph. Therefore, these methods do not guarantee that the final result is a global optimal solution. This paper proposes a graph regularized low-rank representation for semi-supervised learning, called GLR2S2. This method combines the construction of affinity graph with semi supervised learning and unifies them into an optimization framework. By solving the joint optimization problem, the global optimal solution can be obtained. Experimental results on several standard data sets show that the GLR2S2 method proposed in this paper is effective.

Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l 1 -norm of the data matrix while LRR promotes a low-rank structure through minimizing the nuclear norm. In this paper, considering the problem of fitting a union of subspace to a collection of data points drawn from one more subspaces and corrupted by noise, we pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise. We propose a new algorithm, named Low-Rank and Sparse Subspace Clustering with a Clean dictionary (LRS2C2), by combining SSC and LRR, as the representation is often both sparse and low-rank. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and image clustering.

This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing. Exploring information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. The aim is to search for clustering patterns that perform a consensus between the patterns from different views. Inspired by variable weighting and co-regularized strategy, this paper studies co-regularized weighting multiview clustering algorithms. Two co-regularized weighting multiview clustering algorithms are proposed from two aspects: pairwise co-regularization and centroid-based co-regularization. Experimental results obtained both on synthetic and real datasets show that the proposed algorithms outperform the main existing multiview clustering algorithms.

In order to get better clustering precision, the traditional clustering algorithms usually need the support of large amount of historical data. The impact it brings about is: the previous clustering algorithm seems not effective if there exists some information losses in the current situation data collection and the division relationship between datasets is not significant. In this study, a novel clustering technique called transfer entropy weighting soft subspace clustering algorithm (T_EWSC) is proposed by employing the historical information. The properties of this algorithm are investigated and performance is evaluated experimentally using real datasets, including UCI benchmarking datasets, high dimensional gene expression datasets. The experimental results demonstrate that the proposed algorithm is able to use historical information to make up for the inadequacy of the current information and perform well.

Low-Rank Representation (LRR) and Sparse Subspace Clustering (SSC) are considered as the hot topics of subspace clustering algorithms. SSC induces the sparsity through minimizing the l1-norm of the data matrix while LRR promotes a low-rank structure through minimizing the nuclear norm. In this paper, considering the problem of fitting a union of subspace to a collection of data points drawn from one more subspaces and corrupted by noise, we pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise. We propose a new algorithm, named Low-Rank and Sparse Subspace Clustering with a Clean dictionary (LRS2C2), by combining SSC and LRR, as the representation is often both sparse and low-rank. The effectiveness of the proposed algorithm is demonstrated through experiments on motion segmentation and image clustering. [ABSTRACT FROM AUTHOR]