Matrix completion with weighted constraint for haplotype estimation

Majidian, Sina; Mohades, Mohamad Mahdi; Kahaei, Mohammad Hossein

doi:10.1016/j.dsp.2020.102880

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Solving (8) with the said discrete constraints is difficult. Therefore, we propose a convex relaxation of the problem (8), that allows entries of X to lie between 0 and 1. The problem then reads:…”

Section: Formulationmentioning

confidence: 99%

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Binary matrix completion on graphs: Application to collaborative filtering

Talwar

Mongia²,

Chouzenoux³

et al. 2022

Digital Signal Processing

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This work addresses the problem of completing a partially observed matrix where the entries are either ones or zeroes. This is typically called one-bit matrix completion or binary matrix completion. In this problem, the association among the rows and among the columns can be modeled through graph Laplacians. Since the Laplacians cannot be computed from the incomplete matrix, they must be simultaneously estimated while completing the matrix.We model the problem as graph regularized binary matrix completion where the graphs need to be learnt from the data. We proposed an algorithm based on an alternating minimization scheme, taking advantage of an efficient proximity-based inner solver. The algorithm is applied to the problem of collaborative filtering. Experiments on benchmark datasets with state-of-the-art techniques in collaborative filtering show that the proposed method improves over the rest by a considerable margin.

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Section: Formulationmentioning

confidence: 99%

“…Those matrices were then recovered by matrix factorization [4,5] or its deep version [6]. Another approach is via nuclear norm minimization [7,8] where the matrix is directly recovered by promoting a low-rank solution. Since rank minimization is known to be NP-hard, its convex surrogate (nuclear norm) is minimized instead.…”

Section: Introductionmentioning

confidence: 99%

Binary matrix completion on graphs: Application to collaborative filtering

Talwar

Mongia²,

Chouzenoux³

et al. 2022

Digital Signal Processing

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show abstract

“…Side information for MC is not restricted to information regarding the matrix and the available side information can be about the sampling noise distribution [29]. However, this is not the concern of this work.…”

Section: Introductionmentioning

confidence: 99%

Matrix completion with side information using manifold optimisation

Mohades

Kahaei

2020

IET signal process.

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We solve the Matrix Completion (MC) problem based on manifold optimization by incorporating the side information under which the columns of the intended matrix are drawn from a union of low dimensional subspaces. It is proved that this side information leads us to construct new manifolds, as embedded submanifold of the manifold of constant rank matrices, using which the MC problem is solved more accurately. The required geometrical properties of the aforementioned manifold are then presented for matrix completion. Simulation results show that the proposed method outperforms some recent techniques either based on side information or not.

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