Abstract-An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative sizes of the data dimension and sample size, overcoming a limitation of classical LDA. The optimization problem can be solved analytically by applying the Generalized Singular Value Decomposition (GSVD) technique. The pseudoinverse has been suggested and used for undersampled problems in the past, where the data dimension exceeds the number of data points. The criterion proposed in this paper provides a theoretical justification for this procedure. An approximation algorithm for the GSVD-based approach is also presented. It reduces the computational complexity by finding subclusters of each cluster and uses their centroids to capture the structure of each cluster. This reduced problem yields much smaller matrices to which the GSVD can be applied efficiently. Experiments on text data, with up to 7,000 dimensions, show that the approximation algorithm produces results that are close to those produced by the exact algorithm.
A new class of geometric intersection searching problems is introduced, which generalizes previously-considered intersection searching problems and is rich in applications. In a standard intersection searching problem, a set S of n geometric objects is to be preprocessed so that the objects that are intersected by a query object q can be reported efficiently. In a generalized problem, the objects in S come aggregated in disjoint groups and what is of interest are the groups, not the objects, that are intersected by q. Although this problem can be solved easily by using an algorithm for the standard problem, the query time can be Ω(n) even though the output size is just O(1). In this paper, algorithms with efficient, output-size-sensitive query times are presented for the generalized versions of a number of intersection searching problems, including: interval intersection searching, orthogonal segment intersection searching, orthogonal range searching, point enclosure searching, rectangle intersection searching, and segment intersection searching. In addition, the algorithms are also space-efficient.
Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.
In a generalized intersection searching problem, a set, S, of colored geometrie objects is to be preprocessed so that given some query object, q, the distinct colors of the objects intersected by q can be reported efficiently or the number of such colors can be counted effi.ciently. In the dynamic setting, colored objects can be inserted into or de1eted from S. These problems generalize the well-studied standard intersection searching problems and are rich in applications. Unfortunate1y, the techniques known for the standard problems do not yie1d efficient solutions for the generalized problems. Moreover, previous work [JL92] on generalized problems applies only to the static reporting problems. In thispaper, a uniform framework is presented to solve efficiently the countingjreportingj dynamic versions of a variety of generalized intersection searching problems, including: 1-, 2-, and 3-dimensional range searching, quadrant searching, interval intersection searching, 1-and 2-dimensional point enclosure searching, and orthogonal segment intersection searching.
Abstract, Classes of network topologies are identified in which shortest-path information can be succinctly stored at the nodes, if they are assigned suitable names. The naming allows each edge at a node to be labeled with zero or more intervals of integers, representing all nodes reachable by a shortest path via that edge. Starting with the class of outerplanar networks, a natural hierarchy of networks is established, based on the number of intervals required. The outerplanar networks are shown to be precisely the networks requiring just one interval per edge. An optimal algorithm is given for determining the labels for edges in outerplanar networks.
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