Haw-ren Fang

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Nearest neighbor graphs are widely used in data mining and machine learning. A brute-force method to compute the exact kNN graph takes Θ(dn2) time for n data points in the d dimensional Euclidean space. We propose two divide and conquer methods for computing an approximate kNN graph in Θ(dnt) time for high dimensional data (large d). The exponent t ∈ (1,2)(More)
Many applications in science and engineering lead to models which require solving large-scale fixed point problems, or equivalently, systems of nonlinear equations. Several successful techniques for handling such problems are based on quasi-Newton methods that implicitly update the approximate Jacobian or inverse Jacobian to satisfy a certain secant(More)
When combined with Krylov projection methods, polynomial filtering can provide a powerful method for extracting extreme or interior eigenvalues of large sparse matrices. This general approach can be quite efficient in the situation when a large number of eigenvalues is sought. However, its competitiveness depends critically on a good implementation. This(More)
A one-player, finite, probabilistic game with perfect information can be presented as a bipartite graph. For one-player Can’t Stop, the graph is cyclic and the challenge is to determine the game-theoretical values of the positions in the cycles. In this article we prove the existence and uniqueness of the solution to one-player Can’t Stop, and give an(More)
The identification of genes in biomedical text typically consists of two stages: identifying gene mentions and normalization of gene names. We have created an automated process that takes the output of named entity recognition (NER) systems designed to identify genes and normalizes them to standard referents. The system identifies human gene synonyms from(More)
Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can be time-consuming when the data set is large. This paper presents a multilevel framework to reduce the size of the data set, prior to performing dimension reduction. The algorithm exploits nearest-neighbor graphs. It recursively(More)
In the past decade, a number of nonlinear dimensionality reduction methods using an affinity graph have been developed for manifold learning. This paper explores a multilevel framework with the goal of reducing the cost of unsupervised manifold learning and preserving the embedding quality at the same time. An application to spectral clustering is also(More)
A two-player, finite, probabilistic game with perfect information can be presented as a four-partite graph. For Can’t Stop, the graph is cyclic and the challenge is to determine the game-theoretical values of the positions in the cycles. In a previous paper we have presented our success on tackling one-player Can’t Stop. In this paper we prove the existence(More)
We present a pivoting algorithm for solving linear programs with linear complementarity constraints. Our method generalizes the simplex method for linear programming to deal with complementarity conditions. We develop an anticycling scheme that can verify Bouligand stationarity. We also give an optimizationbased technique to find an initial feasible vertex.(More)
We call a matrix triadic if it has no more than two nonzero off-diagonal elements in any column. A symmetric tridiagonal matrix is a special case. In this paper we consider LXLT factorizations of symmetric triadic matrices, where L is unit lower triangular and X is diagonal, block diagonal with 1×1 and 2×2 blocks, or the identity with L lower triangular. We(More)