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One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data.
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Semidefinite programming approaches for sensor network localization with noisy distance measurements
P. Biswas, T.-C. Liang, K.-C. Toh, Y. Ye, and T.-C. Wang · 2006
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Consistency of spectral clustering
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Empirical graph Laplacian approximation of laplace–beltrami operators: Large sample results
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Theory of semidefinite programming for sensor network localization
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Matrix analysis , volume 169
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The effect of coherence on sampling from matrices with orthonormal columns, and preconditioned least squares problems
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Randomized approximation of the gram matrix: Exact computation and probabilistic bounds
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Tight minimax rates for manifold estimation under hausdorff loss
A. K. Kim and H. H. Zhou · 2015
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Discrete hessian eigenmaps method for dimensionality reduction
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Unconstrained and curvature-constrained shortest-path distances and their approximation
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