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Similarity matrix Input Reconstructed
"... We’re given pairwise similarity information Aij on n variables. We suppose that the data has a serial structure, i.e. there is an underlying order π such that A π(i)π(j) decreases with i − j (Rmatrix) Can we recover π? ..."
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We’re given pairwise similarity information Aij on n variables. We suppose that the data has a serial structure, i.e. there is an underlying order π such that A π(i)π(j) decreases with i − j (Rmatrix) Can we recover π?
Synthetic Similarity Matrix Generation
, 2006
"... Data sets with tens of thousands to millions of items are increasingly common in scientific applications. While there have been numerous advances in processor features and performance, the tools scientists use to process and analyze data rarely take advantage of these to scale to support modern data ..."
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Data sets with tens of thousands to millions of items are increasingly common in scientific applications. While there have been numerous advances in processor features and performance, the tools scientists use to process and analyze data rarely take advantage of these to scale to support modern data sizes. In many cases, the performance available to scientists using common software tools is one or two
Classification Visualization with Shaded Similarity Matrix
"... Shaded similarity matrix has long been used in visual cluster analysis. This paper investigates how it can be used in classification visualization. We focus on two popular classification methods: nearest neighbor and decision tree. Ensemble classifier visualization is also presented for handling lar ..."
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Shaded similarity matrix has long been used in visual cluster analysis. This paper investigates how it can be used in classification visualization. We focus on two popular classification methods: nearest neighbor and decision tree. Ensemble classifier visualization is also presented for handling
doi:10.1093/nar/gkj106 SIMAP: the similarity matrix of proteins
, 2005
"... Similarity Matrix of Proteins (SIMAP) ..."
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 775 (21 self)
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is contained in the socalled kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input spaceclassical model selection
Exact Matrix Completion via Convex Optimization
, 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
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Cited by 869 (26 self)
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by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold
Morita Similar Matrix Rings and their Grothendieck Groups
"... In this work sequences of higher order Morita similar matrix rings are constructed from arbitrarily given Morita contexts and their Grothendieck groups are computed. ..."
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Cited by 2 (1 self)
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In this work sequences of higher order Morita similar matrix rings are constructed from arbitrarily given Morita contexts and their Grothendieck groups are computed.
Resolution Audio Similarity Matrix ABSTRACT
"... The papers at this Convention have been selected on the basis of a submitted abstract and extended precis that have been peer reviewed by at least two qualified anonymous reviewers. This convention paper has been reproduced from the author's advance manuscript, without editing, corrections, or ..."
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The papers at this Convention have been selected on the basis of a submitted abstract and extended precis that have been peer reviewed by at least two qualified anonymous reviewers. This convention paper has been reproduced from the author's advance manuscript, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for the contents. Additional papers may be obtained by sending request and remittance to Audio Engineering Society, 60 East 42 nd Street, New
Similarity matrix processing for music structure analysis
 In Proceedings of the Audio and Music Computing for Multimedia Workshop (AMCMM
, 2006
"... The structure analysis of pop and rock songs from audio signals is conducted via similarity matrix processing in this work. The similarity matrix offers pairwise similarity between any two short intervals of fixed length in a song. We use two similarity matrices to show their diverse characteristics ..."
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Cited by 9 (0 self)
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The structure analysis of pop and rock songs from audio signals is conducted via similarity matrix processing in this work. The similarity matrix offers pairwise similarity between any two short intervals of fixed length in a song. We use two similarity matrices to show their diverse
PARALELL IMPLEMENTATION OF THE SIMILARITY MATRIX USING THREADS
"... Sequence comparison is one of the key concepts in determining the functional properties of proteins. Determining the similarity between a given gene and genes in a database tells much about the behavior of that gene. The classical algorithm for computing the similarity between two sequences uses the ..."
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the dynamic programming matrix and compares the two strings with time complexity O(n2). This paper addresses the challenge of computing the similarity between two DNA molecules with a time complexity less than O(n2) by using multithreaded approach. The algorithm requires the use of a multiprocessor machine
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