메뉴 건너뛰기




Volumn 9, Issue 2, 2015, Pages 2950-2975

Adaptive multinomial matrix completion

Author keywords

Low rank matrix estimation; Matrix completion; Multinomial model

Indexed keywords


EID: 84955498874     PISSN: 19357524     EISSN: None     Source Type: Journal    
DOI: 10.1214/15-EJS1093     Document Type: Article
Times cited : (41)

References (29)
  • 3
    • 77951291046 scopus 로고    scopus 로고
    • A singular value thresholding algorithm for matrix completion
    • J-F. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010.
    • (2010) SIAM Journal on Optimization , vol.20 , Issue.4 , pp. 1956-1982
    • Cai, J.-F.1    Candès, E.J.2    Shen, Z.3
  • 5
    • 84893492266 scopus 로고    scopus 로고
    • A max-norm constrained minimization approach to 1-bit matrix completion
    • T. T. Cai and W-X. Zhou. A max-norm constrained minimization approach to 1-bit matrix completion. J. Mach. Learn. Res., 14:3619–3647, 2013.
    • (2013) J. Mach. Learn. Res. , vol.14 , pp. 3619-3647
    • Cai, T.T.1    Zhou, W.-X.2
  • 6
    • 77952741387 scopus 로고    scopus 로고
    • Matrix completion with noise
    • E. J. Candès and Y. Plan. Matrix completion with noise. Proceedings of the IEEE, 98(6):925–936, 2010.
    • (2010) Proceedings of the IEEE , vol.98 , Issue.6 , pp. 925-936
    • Candès, E.J.1    Plan, Y.2
  • 8
    • 84866721491 scopus 로고    scopus 로고
    • Lifted coordinate descent for learning with trace-norm regularization
    • M. Dudík, Z. Harchaoui, and J. Malick. Lifted coordinate descent for learning with trace-norm regularization. In AISTATS, 2012.
    • (2012) AISTATS
    • Dudík, M.1    Harchaoui, Z.2    Malick, J.3
  • 10
    • 85162455237 scopus 로고    scopus 로고
    • Learning with the weighted trace-norm under arbitrary sampling distributions
    • R. Foygel, R. Salakhutdinov, O. Shamir, and N. Srebro. Learning with the weighted trace-norm under arbitrary sampling distributions. In NIPS, pages 2133–2141, 2011.
    • (2011) NIPS , pp. 2133-2141
    • Foygel, R.1    Salakhutdinov, R.2    Shamir, O.3    Srebro, N.4
  • 11
    • 0004236492 scopus 로고    scopus 로고
    • Johns Hopkins University Press, Baltimore, MD, fourth edition
    • G. H. Golub and C. F. van Loan. Matrix computations. Johns Hopkins University Press, Baltimore, MD, fourth edition, 2013.
    • (2013) Matrix Computations
    • Golub, G.H.1    Van Loan, C.F.2
  • 12
    • 79951886985 scopus 로고    scopus 로고
    • Recovering low-rank matrices from few coefficients in any basis
    • D. Gross. Recovering low-rank matrices from few coefficients in any basis. Information Theory, IEEE Transactions on, 57(3):1548–1566, 2011.
    • (2011) Information Theory, IEEE Transactions On , vol.57 , Issue.3 , pp. 1548-1566
    • Gross, D.1
  • 13
    • 84919816524 scopus 로고    scopus 로고
    • Exponential family matrix completion under structural constraints
    • S. Gunasekar, P. Ravikumar, and J. Ghosh. Exponential family matrix completion under structural constraints. ICML, 2014.
    • (2014) ICML
    • Gunasekar, S.1    Ravikumar, P.2    Ghosh, J.3
  • 14
    • 77955991059 scopus 로고    scopus 로고
    • Robust video denoising using low rank matrix completion
    • J. Hui, L. Chaoqiang, S. Zuowei, and X. Yuhong. Robust video denoising using low rank matrix completion. CVPR, 0:1791–1798, 2010.
    • (2010) CVPR , pp. 1791-1798
    • Hui, J.1    Chaoqiang, L.2    Zuowei, S.3    Yuhong, X.4
  • 17
    • 84859835859 scopus 로고    scopus 로고
    • Rank penalized estimators for high-dimensional matrices
    • O. Klopp. Rank penalized estimators for high-dimensional matrices. Electron. J. Stat., 5:1161–1183, 2011.
    • (2011) Electron. J. Stat. , vol.5 , pp. 1161-1183
    • Klopp, O.1
  • 18
    • 84893673569 scopus 로고    scopus 로고
    • Noisy low-rank matrix completion with general sampling distribution
    • O. Klopp. Noisy low-rank matrix completion with general sampling distribution. Bernoulli, 2(1):282–303, 2 2014.
    • (2014) Bernoulli , vol.2 , Issue.1 , pp. 282-303
    • Klopp, O.1
  • 19
    • 82655171609 scopus 로고    scopus 로고
    • Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
    • V. Koltchinskii, A. B. Tsybakov, and K. Lounici. Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion. Ann. Statist., 39(5):2302–2329, 2011.
    • (2011) Ann. Statist. , vol.39 , Issue.5 , pp. 2302-2329
    • Koltchinskii, V.1    Tsybakov, A.B.2    Lounici, K.3
  • 20
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009.
    • (2009) Computer , vol.42 , Issue.8 , pp. 30-37
    • Koren, Y.1    Bell, R.2    Volinsky, C.3
  • 22
    • 0034345597 scopus 로고    scopus 로고
    • About the constants in Talagrand’s concentration inequalities for empirical processes
    • P. Massart. About the constants in Talagrand’s concentration inequalities for empirical processes. Ann. Probab., 28(2):863–884, 2000.
    • (2000) Ann. Probab. , vol.28 , Issue.2 , pp. 863-884
    • Massart, P.1
  • 23
    • 77956944781 scopus 로고    scopus 로고
    • Spectral regularization algorithms for learning large incomplete matrices
    • R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res., 11:2287–2322, 2010.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 2287-2322
    • Mazumder, R.1    Hastie, T.2    Tibshirani, R.3
  • 24
    • 84862020232 scopus 로고    scopus 로고
    • Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
    • S. Negahban and M. J. Wainwright. Restricted strong convexity and weighted matrix completion: optimal bounds with noise. J. Mach. Learn. Res., 13:1665–1697, 2012.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 1665-1697
    • Negahban, S.1    Wainwright, M.J.2
  • 25
    • 84864315555 scopus 로고    scopus 로고
    • User-friendly tail bounds for sums of random matrices
    • J. A. Tropp. User-friendly tail bounds for sums of random matrices. Found. Comput. Math., 12(4):389–434, 2012.
    • (2012) Found. Comput. Math. , vol.12 , Issue.4 , pp. 389-434
    • Tropp, J.A.1
  • 27
    • 84902133693 scopus 로고    scopus 로고
    • Linear total variation approximate regularized nuclear norm optimization for matrix completion
    • pages Art. ID 765782
    • H. Xu, W. Jiasong, W. Lu, C. Yang, L. Senhadji, and H. Shu. Linear total variation approximate regularized nuclear norm optimization for matrix completion. Abstr. Appl. Anal., pages Art. ID 765782, 8, 2014.
    • (2014) Abstr. Appl. Anal.
    • Xu, H.1    Jiasong, W.2    Lu, W.3    Yang, C.4    Senhadji, L.5    Shu, H.6
  • 28
    • 84888350094 scopus 로고    scopus 로고
    • Seismic data reconstruction via matrix completion
    • Y. Yang, J. Ma, and S. Osher. Seismic data reconstruction via matrix completion. Inverse Probl. Imaging, 7(4):1379–1392, 2013.
    • (2013) Inverse Probl. Imaging , vol.7 , Issue.4 , pp. 1379-1392
    • Yang, Y.1    Ma, J.2    Osher, S.3
  • 29
    • 82555183095 scopus 로고    scopus 로고
    • An ordinal model for predicting personalized item rating distributions
    • RecSys’11, New York, NY, USA, ACM
    • Y. Koren and J. Sill. Ordrec: An ordinal model for predicting personalized item rating distributions. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys’11, pages 117–124, New York, NY, USA, 2011. ACM.
    • (2011) Proceedings of the Fifth ACM Conference on Recommender Systems , pp. 117-124
    • Koren, Y.1    Ordrec, J.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.