3,404 research outputs found

    A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion

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    We consider the problem of reconstructing a low-rank matrix from a small subset of its entries. In this paper, we describe the implementation of an efficient algorithm called OptSpace, based on singular value decomposition followed by local manifold optimization, for solving the low-rank matrix completion problem. It has been shown that if the number of revealed entries is large enough, the output of singular value decomposition gives a good estimate for the original matrix, so that local optimization reconstructs the correct matrix with high probability. We present numerical results which show that this algorithm can reconstruct the low rank matrix exactly from a very small subset of its entries. We further study the robustness of the algorithm with respect to noise, and its performance on actual collaborative filtering datasets.Comment: 26 pages, 15 figure

    Regularization for Matrix Completion

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    We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision, and signal processing. In these contexts, "noise" generically refers to any contribution to the data that is not captured by the low-rank model. In most applications, the noise level is large compared to the underlying signal and it is important to avoid overfitting. In order to tackle this problem, we define a regularized cost function well suited for spectral reconstruction methods. Within a random noise model, and in the large system limit, we prove that the resulting accuracy undergoes a phase transition depending on the noise level and on the fraction of observed entries. The cost function can be minimized using OPTSPACE (a manifold gradient descent algorithm). Numerical simulations show that this approach is competitive with state-of-the-art alternatives.Comment: 5 pages, 3 figures, Conference Versio

    Synthesis and fungicidal studies of niobium(V) complexes with N-alkylphenothiazines

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    Department of Studies in Chemistry, University of Mysore, Mysore-570 006 Manuscript received 31 January 1996, revised 19 August 1996, accepted 18 November 1996 Synthesis and Fungicidal Studies of Niobium(V) Complexes with N-Alkylphenothiazines

    Synthesis and antibacterial studies of lanthanide (III) complexes with aminopromazine

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    New complexes of lanthanide(III) nitrates with aminopromazine, having the general formula Ln(AP)(2)(NO3)(2)]NO3 (Ln = La, Cc, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu and AP = aminopromazine) have been synthesised. The complexes have been screened far antibacterial activities
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