14 papers:
- ICML-2015-Shamir #algorithm #convergence #exponential #probability
- A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate (OS), pp. 144–152.
- KDD-2014-ChenC #incremental #named #rank #set
- LWI-SVD: low-rank, windowed, incremental singular value decompositions on time-evolving data sets (XC, KSC), pp. 987–996.
- ICPR-2012-SrikanthaSM #approach #detection #image
- An SVD-based approach for ghost detection and removal in high dynamic range images (AS, DS, FM), pp. 380–383.
- ICPR-2010-DengLGY
- Recognizing Dance Motions with Segmental SVD (LD, HL, NG, YY), pp. 1537–1540.
- ICPR-2008-GurumoorthyRBR #image #representation
- Beyond SVD: Sparse projections onto exemplar orthonormal bases for compact image representation (KSG, AR, AB, AR), pp. 1–4.
- ICPR-2008-MazharG #design #named #taxonomy
- EK-SVD: Optimized dictionary design for sparse representations (RM, PDG), pp. 1–4.
- ICPR-2008-ZhangHZWLW #robust #visual notation
- SVD based Kalman particle filter for robust visual tracking (XZ, WH, ZZ, YW, XL, QW), pp. 1–4.
- KDD-2008-HuangDLL #clustering #equivalence #higher-order
- Simultaneous tensor subspace selection and clustering: the equivalence of high order svd and k-means clustering (HH, CHQD, DL, TL), pp. 327–335.
- SAC-2007-YavuzT #ambiguity #image
- Improved SVD-DWT based digital image watermarking against watermark ambiguity (EY, ZT), pp. 1051–1055.
- ICPR-v3-2006-ZhuZX #algorithm #implementation
- A Digital Watermarking Algorithm and Implementation Based on Improved SVD (XZ, JZ, HX), pp. 651–656.
- SAC-2006-CalagnaGMJ #robust
- A robust watermarking system based on SVD compression (MC, HG, LVM, SJ), pp. 1341–1347.
- SAC-2005-PolatD #collaboration #privacy
- SVD-based collaborative filtering with privacy (HP, WD), pp. 791–795.
- ICPR-v1-2000-WangT #composition #matrix
- An SVD Decomposition of Essential Matrix with Eight Solutions for the Relative Positions of Two Perspective Cameras (WW, HTT), pp. 1362–1365.
- ICPR-1996-YuCXY #3d #approximate #higher-order
- 3D shape and motion by SVD under higher-order approximation of perspective projection (HY, QC, GX, MY), pp. 456–460.