Decomposing background topics from keywords by principal component pursuit

Min, Kerui; Zhang, Zhengdong; Wright, John; Ma, Yi

doi:10.1145/1871437.1871475

Cited by 75 publications

(45 citation statements)

References 20 publications

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“…Min et al [15] "Decomposing background topics from keywords by principal component pursuit" explained about Principal Component Pursuit that can effectively decomposes the low rank and sparse matrices of low dimensional data. In this paper, explained this method in image processing data analysis application.…”

Section: Literature Surveymentioning

confidence: 99%

Separation of Singing Voice from Music Background

Burute¹,

Mane²

2015

IJCA

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Songs are representation of audio signal and musical instruments. An audio signal separation system should be able to identify different audio signals such as speech, background noise and music. In a song the singing voice provides useful information regarding pitch range, music content, music tempo and rhythm. An automatic singing voice separation system is used for attenuating or removing the music accompaniment. The paper presents survey of the various algorithm and method for separating singing voice from musical background. From the survey it is observed that most of researchers used Robust Principal Component Analysis method for separation of singing voice from music background, by taking into account the rank of music accompaniment and the sparsity of singing voices.

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Section: Literature Surveymentioning

confidence: 99%

Separation of Singing Voice from Music Background

Burute¹,

Mane²

2015

IJCA

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“…Since A * is a fixed point of the map ψ in (25), the second equation in (26) can be written as ψ(A * ) = P M (Z − B * ). Then we have…”

Section: Application To Background-foreground Separation Of Surveillamentioning

confidence: 99%

“…The RPCP and its variants have found various promising applications, particularly in image and signal processing; e.g. video surveillance [10], face recognition [17], texture modeling [35], video inpainting [16], audio separation [15], latent semantic indexing [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds

Hintermüller

2014

J Math Imaging Vis

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Robust principal component pursuit (RPCP) refers to a decomposition of a data matrix into a low-rank component and a sparse component. In this work, instead of invoking a convex-relaxation model based on the nuclear norm and the 1 -norm as is typically done in this context, RPCP is solved by considering a least-squares problem subject to rank and cardinality constraints. An inexact alternating minimization scheme, with guaranteed global convergence, is employed to solve the resulting constrained minimization problem. In particular, the low-rank matrix subproblem is resolved inexactly by a tailored Riemannian optimization technique, which favorably avoids singular value decompositions in full dimension. For the overall method, a corresponding q-linear convergence theory is established. The numerical experiments show that the newly proposed method compares competitively with a popular convex-relaxation based approach.

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“…As a consequence, the basic model D ≈ BS is no longer appropriate as there remains large amount of variation in the data left unexplained by the model. To address this issue, [12] proposed the joint topic-document model, D = L + E, where L is the low-rank matrix capturing the background, or topic information, and E is a sparse matrix, representing the document-specific keywords or keypharses that cannot be explained by the (low-rank) topic model.…”

Section: Issues With Current Approachmentioning

confidence: 99%