Boosting Incremental Semi-supervised Discriminant Analysis for Tracking

Cartoon-Texture decomposition with patch-wise decorrelation

Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.

The corrugated layer structure bismuth has been successfully tailored into negative thermal expansion along c axis by size effect. Pair distribution function and extended X-ray absorption fine structure are combined to reveal the local structural distortion for nanosized bismuth. The comprehensive method to identify the local structure of nanomaterials can benefit the regulating and controlling of thermal expansion in nanodivices.

For classifying images with various appearances, graph embedding based subspace learning has difficulty in taking a comprehensive consideration of both local geometrical structure and between-class discriminative information. In addition, when no sufficient training samples exist, using only the simple weight graph corresponding to labeled samples, the embedding subspace may not be accurately modeled. We present a semisupervised graph embedding algorithm by combining graph embedding and sparse representation. This algorithm can effectively learn a compact and semantic subspace by using a locally connected graph, which can model the geometrical structure and essential correlation of subclusters within a class and can fully utilize both labeled and unlabeled samples. Moreover, using L2,1-norm, the proposed algorithm can preserve the sparse representation property of images from the original space in the lower dimensional projected space. Our experiments demonstrate that the proposed algorithm has better performance than the alternatives reported in recent literature.

Dimensionality reduction via preserving local information

For the clustering of subspaces and multi-manifolds in low-dimensional space, this paper adopts Spectral Multi-Manifold Clustering (SMMC) to achieve mixed-manifold clustering. From the perspective of similarity matrix, we effectively use natural local geometric structure information in the manifold sampling points to assist in constructing a more appropriate similarity matrix and then find the correct manifold clustering. This paper establishes a sparse subspace clustering (SSC) model for linear problems to perform cluster analysis, then creates a spectral multi-manifold clustering (SMMC) model for nonlinear problems to perform mixed manifold clustering analysis. Such model effectively separates two straight lines that do not intersect at the origin but perpendicular to each other into two categories; separates a plane and two straight lines into three categories; separates two disjoint quadratic curves into two categories; separates two intersecting spirals into two categories.

A locally weighted sparse graph regularized Non-Negative Matrix Factorization method

In this paper, in terms of pairwise constraints which specify whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints), we propose a novel semi-supervised discriminant analysis algorithm which integrates both global and local structures. Specifically, our objective is to learn a smooth as well as discriminative subspace. In order to achieve it, we jointly use both the instances in the cannot-link constraints to maximize the separability between different classes while applying those in the must-link constraints to minimize the distance between the same class and the integration of global and local structures of the data to make nearby instances in the original space close to each other in the embedding space. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

Electrochemical performance of manganese hexacyanoferrate cathode material in aqueous Zn-ion battery

Abstract. In this paper, we proposed a novel 3D deep learning model for object localization and object bounding boxes estimation. To increase the detection efficiency of small objects in the large scale scenes, the local neighbourhood geometric structure information of objects has been taken into the Edgeconv model, which can operate the original point clouds. We evaluated the 3D bounding box with high resolution in the RGB-D dataset and acquired stable effectiveness even under the sparse points and the strong occlusion. The experimental results indicate that our method achieved the higher mean average precision and better IOU of bounding boxes in SUN RGB-D dataset and KITTI benchmark.

BackgroundIn recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates.The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information.ResultsThe step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88.ConclusionThe described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.

In the Fishervoice (FSH) based framework, the mean supervectors of the speaker models are divided into several subvectors by mixture index. However, this division strategy cannot capture local acoustic class structure information among similar acoustic classes or discriminative information between different acoustic classes. In order to verify whether or not local structure information can help improve system performance, we develop five different speaker supervector segmentation methods. Experiments on NIST SRE08 prove that clustering similar acoustic classes together improves the system performance. In particular, the proposed method of equal size clustering achieves 5.1% relative decrease on EER compared to FSH1.

Supervised feature extraction algorithms usually require lots of labeled samples to achieve good performance. However, labeling the samples is often time-consuming and even impractical. Therefore, in this paper, a semi-supervised manifold local Fisher discriminant analysis (SMLFDA) is proposed to take advantage of unlabeled samples as well as labeled samples. The proposed algorithm utilizes local scatter matrix and manifold structure to extract the information from labeled and unlabeled samples, respectively, which significantly improves the accuracy of successive classification application when labeled samples are insufficient. In addition, an exponential form weighting coefficient is proposed to further improve the classification performance. Experiments of hyperspectral classification demonstrate the effectiveness of the proposed semi-supervised feature extraction algorithm.

Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and a moderate correlation coefficient results in the most efficient information spreading. Incorporating the local informed density information into contact strategy, the convergence time of information spreading can be further reduced, and be minimized by an moderately preferential selection.

Fault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low‐dimensional feature space. Compared to kernel methods, explicit low‐dimensional random Fourier features considerably reduce the computational cost, particularly for real‐time fault diagnosis for a large training dataset or a large‐scale process. Additionally, the local structure information contained in the randomized low‐dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron‐marking process state is determined using Bayesian inference. Case studies on a real‐world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.