Fast semi-supervised clustering with enhanced spectral embedding

In recent years, semi-supervised clustering (SSC) has aroused considerable interests from the machine learning and data mining communities. In this paper we propose a novel SSC approach with enhanced spectral embedding (ESE), which not only considers the geometric structure information contained in data sets, but also can make use of the given side information such as pairwise constraints. Specially, we first construct a symmetry-favored k-NN graph, which is highly robust to noise and outliers, and can reflect the underlying manifold structures of data sets. Then we learn the enhanced spectral embedding towards an ideal data representation as consistent with the given pairwise constraints as possible. Finally, by using the regularization of spectral embedding we formulate learning the new data representation as a semidefinite-quadratic-linear programming (SQLP) problem, which can be efficiently solved. Experimental results on a variety of synthetic and real-world data sets show that our ESE approach outperforms the state-of-the-art SSC algorithms in terms of speed and quality on both vector-based and graph-based clustering.     

An evidential reasoning based classification algorithm and its application for face recognition with class noise

For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm—Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance.     

Fast Fisher Sparsity Preserving Projections

Recently, there has been a lot of interest in the underlying sparse representation structure in high-dimensional data such as face images. In this paper, we propose two novel efficient dimensionality reduction methods named Fast Sparsity Preserving Projections (FSPP) and Fast Fisher Sparsity Preserving Projections (FFSPP), respectively, which aim to preserve the sparse representation structure in high-dimensional data. Unlike the existing Sparsity Preserving Projections (SPP), where the sparse representation structure is learned through resolving n (the number of samples) time-consuming $ \ell^{ 1} $ norm optimization problems, FSPP constructs a dictionary through classwise PCA decompositions and learns the sparse representation structure under the constructed dictionary through matrix–vector multiplications, which is much more computationally tractable. FFSPP takes into consideration both the sparse representation structure and the discriminating efficiency by adding the Fisher constraint to the FSPP formulation to improve FSPP’s discriminating ability. Both of the proposed methods can boil down to a generalized eigenvalue problem. Experimental results on three publicly available face data sets (Yale, Extended Yale B and ORL), and a standard document collection (Reuters-21578) validate the feasibility and effectiveness of the proposed methods.     

MIG钎焊堆焊铜带技术

针对机械加工制成铜带的不足之处采用MIG钎焊方法堆焊铜带.试验表明,焊缝成形良好,并且与钢实现了冶金结合,界面与铜带无缺陷.探讨了焊接工艺参数对堆焊金属化学成分和力学性能的影响,形成了较好的工艺技术,其产品完全满足生产要求.     

个人信用消费在我国的发展与思考

论述了个人信用消费的作用、产生,阐述了我国个人信用消费的现状及影响个人信用消费的几个因素,提出了发展个人信用消费的建议。     

基于灰色关联分析的小波神经网络及应用

运用灰色理论中的关联分析法对小波神经网络输入权值进行选取,拓展了小波神经网络的应用范围,通过理论分析和变参数振动钻削过程的预测实验表明,该方法是可行的,这为叠层材料振动钻削研究提供了新的方法。     

Robust Positive semidefinite L-Isomap Ensemble

In this paper, we derive an ensemble method inspired by boosting, a novel Robust Positive semidefinite L-Isomap Ensemble (RPL-IsomapE) approach. Specifically, we first apply a constant-shifting method to yield a symmetric positive semidefinite (SPSD) matrix. For topological stability, we also employ a method for eliminating critical outlier points using the confusion rate of all the data points. Then we align individual Robust Positive semidefinite L-Isomap (RPL-Isomap) solutions in common coordinate system through high dimensional affine transformations. Finally, we combine multiple RPL-Isomap solutions by the weighted averaging procedure according to residual variance to improve the noise-robustness of our method. Our RPL-IsomapE maintains the scalability and the speed of L-Isomap. Experiments on two images data sets and a video data set confirm the promising performance of the proposed RPL-IsomapE.     

热中子分析探雷技术的实验研究

为验证热中子分析(Thermal neutron analysis,TNA)方法用于地雷探测的可行性,探索并掌握该方法的技术特点。采用国产新型LaBr3(Ce)快闪烁体探测器,搭建了一套基于252Cf同位素中子源的热中子分析探雷实验系统,主要包含慢热中子转化系统、屏蔽系统、探测系统三个部分。在此基础上,研究了宽能量范围特别是对高能段的精确刻度方法,提出了基于N(H)元素10.83 MeV(2.22 MeV)特征信号计数异常的地雷最小可探测时间。在黄土、粘土、沙土以及磁性土的实验雷池中对72式反坦克地雷、500 g TNT样品以及多种干扰物进行了测试。结果表明,该套探雷实验系统可实现不同背景下的地雷探测,并能有效判弃传统探雷技术易产生虚警的干扰物。