2162472
Block-level link analysis
Link Analysis has shown great potential in improving the performance of web search. PageRank and HITS are two of the most popular algorithms. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page contains multiple semantics and hence the web page might not be considered as the atomic node. In this paper, the web page is partitioned into blocks using the vision-based page segmentation algorithm. By extracting the page-to-block, block-to-page relationships from link structure and page layout analysis, we can construct a semantic graph over the WWW such that each node exactly represents a single semantic topic. This graph can better describe the semantic structure of the web. Based on block-level link analysis, we proposed two new algorithms, Block Level PageRank and Block Level HITS, whose performances we study extensively using web data.
1623769
Orthogonal locality preserving indexing
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is extremely sensitive to the number of dimensions. This makes it difficult to estimate the intrinsic dimensionality, while inaccurately estimated dimensionality would drastically degrade its performance. One reason leading to this problem is that LPI is non-orthogonal. Non-orthogonality distorts the metric structure of the document space. In this paper, we propose a new algorithm called Orthogonal LPI. Orthogonal LPI iteratively computes the mutually orthogonal basis functions which respect the local geometrical structure. Moreover, our empirical study shows that OLPI can have more locality preserving power than LPI. We compare the new algorithm to LSI and LPI. Extensive experimental results show that Orthogonal LPI obtains better performance than both LSI and LPI. More crucially, it is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc.
3132633
Tensor Subspace Analysis
Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear sub-space learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). All of these methods consider an n1 x n2 image as a high dimensional vector in ℝn1 x n2, while an image represented in the plane is intrinsically a matrix. In this paper, we propose a new algorithm called Tensor Subspace Analysis (TSA). TSA considers an image as the second order tensor in Rn1 ⊗ Rn2, where Rn1 and Rn2 are two vector spaces. The relationship between the column vectors of the image matrix and that between the row vectors can be naturally characterized by TSA. TSA detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace. We compare our proposed approach with PCA, LDA and LPP methods on two standard databases. Experimental results demonstrate that TSA achieves better recognition rate, while being much more efficient.
6559152
Tensor space model for document analysis
Vector Space Model (VSM) has been at the core of information retrieval for the past decades. VSM considers the documents as vectors in high dimensional space.In such a vector space, techniques like Latent Semantic Indexing (LSI), Support Vector Machines (SVM), Naive Bayes, etc., can be then applied for indexing and classification. However, in some cases, the dimensionality of the document space might be extremely large, which makes these techniques infeasible due to the curse of dimensionality. In this paper, we propose a novel Tensor Space Model for document analysis. We represent documents as the second order tensors, or matrices. Correspondingly, a novel indexing algorithm called Tensor Latent Semantic Indexing (TensorLSI) is developed in the tensor space. Our theoretical analysis shows that TensorLSI is much more computationally efficient than the conventional Latent Semantic Indexing, which makes it applicable for extremely large scale data set. Several experimental results on standard document data sets demonstrate the efficiency and effectiveness of our algorithm.
12494221
Statistical and computational analysis of locality preserving projection
Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap (Belkin & Niyogi, 2001), Locality Preserving Projection (LPP) (He & Niyogi, 2003), etc. All of them aim at discovering the meaningful low dimensional structure of the data space. In this paper, we present a statistical analysis of the LPP algorithm. Different from Principal Component Analysis (PCA) which obtains a subspace spanned by the largest eigenvectors of the global covariance matrix, we show that LPP obtains a subspace spanned by the smallest eigenvectors of the local covariance matrix. We applied PCA and LPP to real world document clustering task. Experimental results show that the performance can be significantly improved in the subspace, and especially LPP works much better than PCA.
14516443
Learning a semantic space from user's relevance feedback for image retrieval
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
37910692
Replicated shared object model for parallel edge detection algorithm based on spiral architecture
Abstract Edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as discontinuities in intensities. A parallel edge detection algorithm based on spiral architecture is designed in this paper using a replicated shared object model (RSOM). We use this approach to meet the requirements of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects. Multi-scale image theory plays an important role in this research.
18826487
On Even Triangulations of 2-Connected Embedded Graphs
Recently, Hoffmann and Kriegel proved an important combinatorial theorem [4]: Every 2-connected bipartite plane graph G has a triangulation in which all vertices have even degree (it's called an even triangulation). Combined with a classical Whitney's Theorem, this result implies that every such a graph has a 3-colorable plane triangulation. Using this theorem, Hoffmann and Kriegel significantly improved the upper bounds of several art gallery and prison guard problems. In [7], Zhang and He presented a linear time algorithm which relies on the complicated algebraic proof in [4]. This proof cannot be extended to similar graphs embedded on high genus surfaces. It's not known whether Hoffmann and Kriegel's Theorem is true for such graphs. 
 
In this paper, we describe a totally independent and much simpler proof of the above theorem, using only graph-theoretic arguments. Our new proof can be easily extend to show the existence of even triangulations for similar graphs on high genus surfaces. Hence we show that Hoffmann and Kriegel's theorem remains valid for such graphs. Our new proof leads to a very simple linear time algorithm for finding even triangulations.
2341522
PZ nets a formal method integrating Petri nets with Z
Abstract In this paper, a formal method (called PZ nets) for specifying concurrent and distributed systems is presented. PZ nets integrate two well-known existing formal methods Petri nets and Z such that Petri nets are used to specify the overall structure, control flows, causal relation, and dynamic behavior of a system; and Z is used to define tokens, labels and constrains of the system. The essence, benefits, and problems of the integration are discussed. A set of heuristics and transformations to develop PZ nets and a technique to analyze PZ nets are proposed and demonstrated through a well-known example.
1887353
An Application of Well-Orderly Trees in Graph Drawing
Well-orderly trees seem to have the potential of becoming a powerful technique capable of deriving new results in graph encoding, graph enumeration and graph generation [3, 4]. In this paper, we reduce the height of the visibility representation of plane graphs from 5n/6 to (4n–1)/5, by using well-orderly trees.
14218377
A Methodology for Constructing Predicate Transition Net Specifications
In this paper, a methodology for constructing hierarchical and structured predicate transition net specifications is developed, which includes new systematic notation extensions for supporting various transformation techniques upon predicate transition nets and several rules for applying such transformation techniques. The levelling technique in data-flow diagrams is adapted in the refinement and the abstraction techniques, and the state decomposition idea in state-charts is employed in designing various label formulation operators. The methodology is illustrated through the specification of a lift system. The methodology can significantly reduce the constructing complexity and enhance the comprehensibility of large predicate transition net specifications.