Method for Clustering Samples with Weakly Supervised Kernel Mean Shift Matrices
First Claim
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1. A method for clustering samples using a mean shift procedure, comprising the a computer system for performing steps of the method, comprising the steps of:
- determining a kernel matrix from the samples in a first dimension;
determining a constraint matrix and a scaling matrix from a constraint set;
projecting the kernel matrix to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension;
clustering the samples according to the kernel matrix.
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Abstract
A method clusters samples using a mean shift procedure. A kernel matrix is determined from the samples in a first dimension. A constraint matrix and a scaling matrix are determined from a constraint set. The kernel matrix is projected to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension. Then, the samples are clustered according to the kernel matrix.
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15 Claims
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1. A method for clustering samples using a mean shift procedure, comprising the a computer system for performing steps of the method, comprising the steps of:
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determining a kernel matrix from the samples in a first dimension; determining a constraint matrix and a scaling matrix from a constraint set; projecting the kernel matrix to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension; clustering the samples according to the kernel matrix. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15)
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11. The method of 1, wherein the samples lie on arbitrary manifolds.
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