Criteria for informative experiments with subspace identification

Informative experiments are identification experiments which contain sufficient information for an identification algorithm to discriminate between different models in an intended model set. In this paper, a particular set of identification algorithms, namely subspace based identification, is considered. Criteria for experiments to be informative with these methods in the deterministic setup and the combined deterministic-stochastic setup are presented. It is pointed out that if these criteria are not satisfied, interesting phenomena, in which perfect cancellations of the deterministic components and the stochastic components occur in a subspace projection, may occur. It is further shown that such cancellations can indeed be avoided under mild conditions.     

Sparse sample self-representation for subspace clustering

This paper proposes a new subspace clustering method based on sparse sample self-representation (SSR). The proposed method considers SSR to solve the problem that affinity matrix does not strictly follow the structure of subspace, and also utilizes sparse constraint to ensure the robustness to noise and outliers in subspace clustering. Specifically, we propose to first construct a self-representation matrix for all samples and combine an l ₁-norm regularizer with an l _2,1-norm regularizer to guarantee that each sample can be represented as a sparse linear combination of its related samples. Then, we conduct the resulting matrix to build an affinity matrix. Finally, we apply spectral clustering on the affinity matrix to conduct clustering. In order to validate the effectiveness of the proposed method, we conducted experiments on UCI datasets, and the experimental results showed that our proposed method reduced the minimal clustering error, outperforming the state-of-the-art methods.

     

基于子空间方法的应力影响下变异语音分类

应力影响下的变异语音是由于说话人受到重力加速度变化而产生的,与正常语音相比,变异语音频谱能量在频带范围内分布更加分散。把整个频带划分成8个子带,采用子带频谱能量的比值为特征,提出一种基于子空间方法的正常/变异语音分类方法。该方法采用CLAFIC方法设计初始向量子空间,并通过LSM算法对两类样本子空间按不同的旋转方式训练,用预分类的结果调整分类器的参数来改善分类器的性能。实验结果表明,该方法对应力影响下的变异语音与正常语音具有良好分类效果,平均分类正确率达到了95.9%。     

核子空间样本选择方法的核最近邻凸包分类器

为了保证核最近邻凸包分类器有效地处理大训练集的应用问题,提出一种核子空间样本选择方法与该分类器相结合。核子空间样本选择方法是一个类内迭代算法,该算法在核空间里每次迭代选择一个距离选择集样本张成子空间最远的样本。在MIT-CBCL人脸识别数据库的training-synthetic子库上的实验中,该方法不但可以取得100%的识别率,而且与未经选样的核最近邻凸包分类器相比,其执行速度要快许多。     

Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

Summarization – compressing data into an informative representation

In this paper, we formulate the problem of summarization of a data set of transactions with categorical attributes as an optimization problem involving two objective functions – compaction gain and information loss. We propose metrics to characterize the output of any summarization algorithm. We investigate two approaches to address this problem. The first approach is an adaptation of clustering and the second approach makes use of frequent itemsets from the association analysis domain. We illustrate one application of summarization in the field of network data where we show how our technique can be effectively used to summarize network traffic into a compact but meaningful representation. Specifically, we evaluate our proposed algorithms on the 1998 DARPA Off-Line Intrusion Detection Evaluation data and network data generated by SKAION Corp for the ARDA information assurance program. Vipin Kumar is currently William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota. His research interests include high-performance computing and data mining. He has authored over 200 research articles, and has coedited or coauthored nine books including the widely used text booksIntroduction to Parallel Computing andIntroduction to Data Mining, both published by Addison Wesley. He has served as chair/co-chair for many conferences/workshops in the area of data mining and parallel computing, including the IEEE International Conference on Data Mining (2002) and the 15th International Parallel and Distributed Processing Symposium (2001). He serves as the chair of the steering committee of the SIAM International Conference on Data Mining, and is a member of the steering committee of the IEEE International Conference on Data Mining. Dr. Kumar serves or has served on the editorial boards of several journals includingKnowledge and Information Systems,Journal of Parallel and Distributed Computing andIEEE Transactions of Data and Knowledge Engineering (1993–1997). He is a Fellow of the ACM and IEEE, and a member of SIAM. Varun Chandola received his BTech degree in Computer Science from the Indian Institute of Technology, Madras, India, in 2002. He is currently a PhD student in the Computer Science and Engineering Department at the University of Minnesota. His research interests include data mining, cyber-security and machine learning.     

Closed-loop subspace identification: an orthogonal projection approach

In this paper, a closed-loop subspace identification approach through an orthogonal projection and subsequent singular value decomposition is proposed. As a by-product of this development, it explains why some existing subspace methods may deliver a bias in the presence of the feedback control and suggests a remedy to eliminate the bias. Furthermore, as the proposed method is a projection based method, it can simultaneously provide extended observability matrix, lower triangular block-Toeplitz matrix, and Kalman filtered state sequences. Therefore, using this method, the system state space matrices can be recovered either from the extended observability matrix/the block-Toeplitz matrix or from the Kalman filter state sequences.     

An algorithm for information projection to an affine subspace

We investigate an algorithm to find a point of an affine subspace in the positive orthant such that it is the closest one to the original point with respect to the Kullback–Leibler distance. This problem is solved by means of the classical Darroch–Ratcliff algorithm (see [1]), while we use ideas of the information geometry founded by Chentsov (see [2]) and Csiszar (see [3]). The main theorem of the present work proves the convergence of that algorithm (the method of the proof is different from previous ones). The proposed algorithm can be applied, e.g., to find the maximum likelihood estimates in an exponential family (see the last section of the paper).     

Adaptive subspace learning: an iterative approach for document clustering

The performance of clustering in document space can be influenced by the high dimension of the vectors, because there exists a great deal of redundant information in the high-dimensional vectors, which may make the similarity between vectors inaccurate. Hence, it is very considerable to derive a low-dimensional subspace that contains less redundant information, so that document vectors can be grouped more reasonably. In general, learning a subspace and clustering vectors are treated as two independent steps; in this case, we cannot estimate whether the subspace is appropriate for the method of clustering or vice versa. To overcome this drawback, this paper combines subspace learning and clustering into an iterative procedure named adaptive subspace learning (ASL). Firstly, the intracluster similarity and the intercluster separability of vectors can be increased via the initial cluster indicators in the step of subspace learning, and then affinity propagation is adopted to partition the vectors into a specific number of clusters, so as to update the cluster indicators and repeat subspace learning. In ASL, the obtained subspace can become more suitable for the clustering with the iterative optimization. The proposed method is evaluated using NG20, Classic3 and K1b datasets, and the results are shown to be superior to the conventional methods of document clustering.     

The ALSM algorithm — an improved subspace method of classification

The subspace methods of classification are decision-theoretic pattern recognition methods in which each class is represented in terms of a linear subspace of the Euclidean pattern or feature space. In most reported subspace methods, a priori criteria have been applied to improve either the class representation or the discriminatory power of the subspaces. Recently, construction of the class subspaces by learning has been suggested by Kohonen, resulting in an improved classification accuracy. A variant of the original learning rule is analyzed and results are given on its application to the classification of phonemes in automatic speech recognition.     

Constructing an exactly periodic subspace for enhancing SSVEP based BCI

A novel exactly periodic spatial filtering (EPSD) approach, that provides a robust detection performance, is introduced and evaluated in this study. The proposed method exploits the temporal properties of the steady-state visual evoked potential (SSVEP) response to construct an orthogonal and exactly periodic mapping that enhances the signal to noise ratio (SNR) of the SSVEP embedded in the electroencephalogram (EEG) data. The subspace of interest is constructed via the elimination of the signals spaces that does not constitute the exact period of the target frequency. The EPSD is evaluated on a 35 subject benchmark dataset collected using a 40 target SSVEP BCI system. The results reveal that the proposed EPSD spatial filter significantly enhances the performance of target detection. Further statistical tests also confirm that the EPSD is a potential alternative to the existing SSVEP spatial filters for realizing an efficient BCI system.     

大系统的分散能控子空间与分散不能观测子空间

提出了大系统的分散能控子空间和分散不能观测子空间的概念.研究了它们分别与集中控制中的能控子空间和不能观测子空间之间的关系.研究中借用了几何控制理论中的方法.结果表明,分散能控子空间和分散不能观测子空间是集中控制中能控子空间和不能观测子空间在分散控制下的自然推广.利用这两个概念,可以从几何角度研究大系统分散控制的几个问题,比如时变分散控制下的系统镇定问题.     

A constrained optimization approach for an adaptive generalized subspace tracking algorithm

In this paper, we present an orthonormal version of the generalized signal subspace tracking. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the C_x-orthonormality of the estimated generalized signal subspace basis at each iteration which C_x denotes the correlation matrix of the sequence x(t). An efficient implementation of the proposed algorithm enhances applicability of it in real time applications.     

Comparing subspace clusterings

We present the first framework for comparing subspace clusterings. We propose several distance measures for subspace clusterings, including generalizations of well-known distance measures for ordinary clusterings. We describe a set of important properties for any measure for comparing subspace clusterings and give a systematic comparison of our proposed measures in terms of these properties. We validate the usefulness of our subspace clustering distance measures by comparing clusterings produced by the algorithms FastDOC, HARP, PROCLUS, ORCLUS, and SSPC. We show that our distance measures can be also used to compare partial clusterings, overlapping clusterings, and patterns in binary data matrices.     

Matching weak informative ontologies

Dear editor,Most existing ontology matching methods utilize literal in-formation to discover alignments[1,2].However,some lit-eral information in ontologies ma...     

Qualitative investigation of a generalized information model of an informative educational telecommunication environment

A generalized information model (GIM) of an informative educational telecommunication environment (IETE) is considered. Such a GIM is a first-order autonomous dynamic system. The results of a qualitative analysis of the model are presented that underlie the formulation of key characteristics of the IETE. These characteristics can be the basis for classification of IETEs. The safety margins for the parameters of the GIM are determined. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 177–187, May–June 2006.     

Face recognition based on random subspace method and tensor subspace analysis

In this paper, we propose a novel method, called random subspace method (RSM) based on tensor (Tensor-RS), for face recognition. Different from the traditional RSM which treats each pixel (or feature) of the face image as a sampling unit, thus ignores the spatial information within the face image, the proposed Tensor-RS regards each small image region as a sampling unit and obtains spatial information within small image regions by using reshaping image and executing tensor-based feature extraction method. More specifically, an original whole face image is first partitioned into some sub-images to improve the robustness to facial variations, and then each sub-image is reshaped into a new matrix whose each row corresponds to a vectorized small sub-image region. After that, based on these rearranged newly formed matrices, an incomplete random sampling by row vectors rather than by features (or feature projections) is applied. Finally, tensor subspace method, which can effectively extract the spatial information within the same row (or column) vector, is used to extract useful features. Extensive experiments on four standard face databases (AR, Yale, Extended Yale B and CMU PIE) demonstrate that the proposed Tensor-RS method significantly outperforms state-of-the-art methods.

     

基于快速凸无穷范数极小化的 大量子空间的子空间分割

摘 要：子空间分割是计算机视觉和机器学习中的一个基本问题。由于实际问题中的数据 往往类数较多,使得大量子空间的子空间分割问题显得尤为重要。近年来基于谱聚类的方法在 子空间分割领域得到了越来越多的关注,但是在相关工作的实验中,子空间的个数却往往不超 过 10 个。无穷范数极小化是近年来提出的一个专门针对大量子空间的子空间分割问题的方法, 其通过降低表示系数矩阵的差异性能有效地处理该问题,但是仍有一定的局限,例如计算速度 仍不够快,缺乏针对独立子空间问题的理论保证。为此,提出快速凸无穷范数极小化,该个方 法不仅能够降低表示系数矩阵的差异性,而且能够对独立子空间情况提供理论保障且计算速度 更快,大量的实验证明了该方法的有效性。     

Towards informative sensor-based grasp planning

This paper proposes a probabilistic framework for sensor-based grasping and describes how information about object attributes, such as position and orientation, can be updated using on-line sensor information gained during grasping. This allows learning about the target object even with a failed grasp, leading to replanning with improved performance at each successive attempt. Two grasp planning approaches utilizing the framework are proposed. Firstly, an approach maximizing the expected posterior stability of a grasp is suggested. Secondly, the approach is extended to use an entropy-based explorative procedure, which allows gathering more information when the current belief about the grasp stability does not allow robust grasping. In the framework, both object and grasp attributes as well as the stability of the grasp and on-line sensor information are represented by probabilistic models. Experiments show that the probabilistic treatment of grasping allows improving the probability of success in a series of grasping attempts. Moreover, experimental results on a real platform using the basic stability maximizing approach not only validate the proposed probabilistic framework but also show that under large initial uncertainties, explorative actions help to achieve successful grasps faster.