Both neighbourhood preserving embedding (NPE) and sparsity preserving projection (SPP) are unsupervised learning methods, where NPE can preserve the local neighbourhood information of a given dataset and SPP can preserve the sparsely reconstructive relationship of the dataset. However, it is not satisfactory when applying the two methods to classification tasks. First, a modified SPP is presented here. Then this study proposes a supervised sparse neighbourhood preserving embedded algorithm (SSNPE) based on NPE, the modified SPP and the label information of a given task. SSNPE inherits the merits of NPE and SPP, which can preserve not only the local neighbourhood information but also the sparsely reconstructive relationship. The connection between SSNPE and both NPE and SPP is discussed. Experimental results on the datasets of UCI, ORL and MNIST indicate that the proposed method is effective.

The recently proposed coupled sequence labeling is shown to be able to effectively exploit multiple labeled data with heterogeneous annotations but suffer from severe inefficiency problem due to the large bundled tag space (Li et al., 2015).In their case study of part-ofspeech (POS) tagging, Li et al. (2015) manually design context-free tag-to-tag mapping rules with a lot of effort to reduce the tag space.This paper proposes a context-aware pruning approach that performs token-wise constraints on the tag space based on contextual evidences, making the coupled approach efficient enough to be applied to the more complex task of joint word segmentation (WS) and POS tagging for the first time.Experiments show that using the large-scale People Daily as auxiliary heterogeneous data, the coupled approach can improve F-score by 95.55 -94.88 = 0.67% on WS, and by 90.58 -89.49= 1.09% on joint WS&POS on Penn Chinese Treebank.All codes are released at http://hlt.suda.edu.cn/~zhli.

Reliable broadcasting for a network can be obtained by using completely independent spanning trees(CISTs). Locally twisted cubes are popular networks which have been studied widely in the literature. In this paper, we study the problem of using CISTs to establish reliable broadcasting in locally twisted cubes. We first propose an algorithm, named LTQCIST, to construct two CISTs in locally twisted cubes, then exemplify the construction procedures to construct CISTs. Finally, we prove the correctness of Algorithm LTQCIST and simulate CISTs with JUNG.

1-norm support vector machine (SVM) has attracted substantial attentions for its good sparsity. However, the computational complexity of training 1-norm SVM is about the cube of the sample number, which is high. This paper replaces the hinge loss or the ε-insensitive loss by the squared loss in the 1-norm SVM, and applies orthogonal matching pursuit (OMP) to approximate the solution of the 1-norm SVM with the squared loss. Experimental results on toy and real-world datasets show that OMP can faster train 1-norm SVM and achieve similar learning performance compared with some methods available.

Supervised locally linear embedding (SLLE) has been proposed for classification tasks. SLLE can take full use of the label information and select neighbours only in the same class. However, SLLE uses the least squares (LSs) method for solving a set of linear equations to obtain linear representation coefficients, which relates to the inverse of a matrix. If the matrix is singular, the solution to the set of linear equations does not exist. Additionally, if the size of neighbourhood is not appropriate, some further neighbours along the manifold would be selected. To remedy those, this study deals with SLLE based on orthogonal matching pursuit (SLLE‐OMP) by introducing OMP into SLLE. In SLLE‐OMP, LS is replaced by OMP and OMP can reselect new neighbours from old ones. Experimental results on some real‐world datasets show that SLLE‐OMP can achieve better classification performance compared with SLLE.

Locally Linear Embedding (LLE) is a sort of powerful nonlinear dimensionality reduction algorithms. The basic idea behind the LLE method is that each data point and its neighbors lie on or close to a locally linear patch of the manifold if there is sufficient data. Then the local geometry of these patches is described by using linear coefficients which can reconstruct each data point from its neighbors. However, LLE operates in a batch way and its dimension reduction cannot be generalized to unseen samples. If a test sample arrives, LLE must run repeatedly and the former computational results are discarded. Thus, some incremental methods have been proposed for LLE to solve this problem. In these incremental methods, the neighbor number is globally fixed, which may result in selecting points from another linear space as neighbors. This paper presents LLE based on orthogonal matching pursuit (OMP) and applies it to classification tasks. In the classification tasks, dimensionality reduction on test samples is implemented by applying dimension reduction on training samples. The new LLE method could select a more appropriate neighbors from the selected neighbors. OMP is applied to not only LLE for training samples, but also the incremental learning of LLE for test samples. Compared with other linear incremental methods, experimental results show that the proposed method is promising.

Deep networks are well known for their powerful function approximations. To train a deep network efficiently, greedy layer-wise pre-training and fine tuning are required. Typically, pre-training, aiming to initialize a deep network, is implemented via unsupervised feature learning, with multiple feature representations generated. However, in general only the last layer representation is to be employed because of its abstraction and compactness being the best with comparisons to the ones of lower layers. To make full use of the representations of all layers, this paper proposes a feature ensemble learning method based on sparse autoencoders for image classification. Specifically, we train three softmax classifiers by using the representations of different layers, instead of one classifier trained by applying the last layer representation. Of the three softmax classifiers, two are obtained by training stacked autoencoders with fine tuning, and the other one is obtained by directly using a concatenation of two representations. To improve accuracy and stability of a single softmax classifier, the ensemble of multiple classifiers is considered, and some Naive Bayes combination rules are introduced to integrate the three classifiers. Experimental results on the MNTST and COIL datasets are presented, with comparisons to other classification methods.

The problem of extending the curvature scale-space (CSS) technique to represent open curves is addressed. Various approaches for dealing with the endpoint problem of open curves are considered, and one is selected which allows us to handle the evolution of the open curves as a special case of the evolution of closed curves. The convergence theory of evolved open curves is established, and the CSS shape representation is investigated.

An efficient algorithm is proposed for detecting vertical corner lines on buildings in quasi-Manhattan world scene. The vertical corner lines are useful geometric information of buildings in urban scene, whose importance is similar to that of corners on planar curves for various applications in computer vision. The algorithm employs a bottom-up and step-by-step pipeline processing procedure. First, straight-line segments are extracted as low-level features from the input building image by using a fast and accurate line segment detector. Secondly, the extracted straight-line segments are clustered to groups and associated to different vanishing points, which are computed by a J-Linkage estimator and act as the mid-level features. Finally, high-level features, vertical corner lines, are detected with several geometric constraints in quasi-Manhattan world. Experimental results demonstrate the efficiency of the new algorithm.

Since the sensor node is important and critical in wireless sensor networks (WSNs), it is necessary to provide wireless sensor network certain level of protection. The self protection problem focuses on using the sensor nodes of the network to protect themselves rather than other objects so that the network can resist attacks. In this paper, we study both the minimum 1-self protection and minimum k-self protection problems, and propose our distributed local optimum algorithms. In addition, we give our method of finding multiple protection sets when the network is redundant, so that the protection sets can work by turns. Experimental result manifests the efficiency and feasibility of our algorithms proposed.

Hybrid sensor network (HSN) is composed of a set of static and mobile sensor nodes. Besides the capabilities of sensing, computing and communicating like static nodes, mobile nodes have locomotion capability particularly. Area coverage is one of the most fundamental metrics for hybrid sensor network because it is crucial to the quality of monitoring. In order to improve the network coverage, we employ the mobility of mobile nodes to compensate for the uneven deployment of static sensors. In this paper we propose a 3-phase algorithm based on Hamilton Circuit. Conducted simulation shows that the algorithm can improve coverage performance with less energy conservation. Experimental results verify the feasibility and validity of the algorithm proposed.