A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure

Esmaeili, Ashkan; Marvasti, Farokh

doi:10.1109/lsp.2019.2891134

Cited by 30 publications

(13 citation statements)

References 22 publications

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“…Let us analyse deeper the results in Table 2. The dataset contains more drugs (86) than viruses (23). In CV1 settings, when entries are randomly omitted, without skipping drugs and viruses our method performs the best.…”

Section: Discussionmentioning

confidence: 99%

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DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

Majumdar¹,

Mongia²,

Chouzenoux³

et al. 2020

Preprint

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This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under for trials for the same.<br>

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Section: Discussionmentioning

confidence: 99%

“…In communications and signal processing, this is actually never the case as those values are almost always quantized. This led to the quantized matrix completion problem [23,6]. One extreme case is binary matrix completion [17] where the matrix entries are represented by only one bit.…”

Section: Matrix Completionmentioning

confidence: 99%

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

Majumdar¹,

Mongia²,

Chouzenoux³

et al. 2020

Preprint

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show abstract

“…Employing the entire closed-set data during the training procedure leads to inclusion of untrustworthy samples of the closed-set. Regularized or underfitting models (such as low-rank representations [70,71,72]) still suffer from memorizing effect of such samples, which exacerbate the separation between open and closed-set by adding ambiguity to the decision boundary between the closed and openset classes. To resolve this issue, we utilize our proposed selection method, KSP, which selects the core representatives.…”

Section: Open-set Identificationmentioning

confidence: 99%

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Joneidi¹,

Vahidian²,

Esmaeili³

et al. 2020

Preprint

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Finding a small subset of data whose linear combination spans other data points, also called column subset selection problem (CSSP), is an important open problem in computer science with many applications in computer vision and deep learning. There are some studies that solve CSSP in a polynomial time complexity w.r.t. the size of the original dataset. A simple and efficient selection algorithm with a linear complexity order, referred to as spectrum pursuit (SP), is proposed that pursuits spectral components of the dataset using available sample points. The proposed non-greedy algorithm aims to iteratively find K data samples whose span is close to that of the first K spectral components of entire data. SP has no parameter to be fine tuned and this desirable property makes it problem-independent. The simplicity of SP enables us to extend the underlying linear model to more complex models such as nonlinear manifolds and graph-based models. The nonlinear extension of SP is introduced as kernel-SP (KSP). The superiority of the proposed algorithms is demonstrated in a wide range of applications.

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“…Also, λ > 0 is a weighting factor, regularizing the sparsity and the rank. There are many suggested approaches to tackle the nuclear norm in optimization 3 such as smooth rank approximation [11], [17], [18].…”

Section: Problem Modelmentioning

confidence: 99%

Robust low rank and sparse decomposition from blurred video frames

Bokaei¹

2020

Preprint

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<div>Recently, the low-rank and sparse decomposition</div><div>problem has attracted attention in several applications, especially surveillance videos. Due to the physical limitations in acquisition systems, measured frames are blurred by a low-pass filter.</div><div>In this article, we aim to decompose blurred videos’ frames</div><div>into low-rank and sparse components, in order to extract the</div><div>background. Unlike conventional methods, we simultaneously take into account the blurring effect, as well as the missing data. Our simulation results confirmed the advantage of this approach in extracting low-rank components in surveillance videos.</div>

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A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure

Cited by 30 publications

References 22 publications

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds

Robust low rank and sparse decomposition from blurred video frames

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