Stem sparsiti$ (all stems)

46 papers:

CASE-2015-BjorkenstamCL

Exploiting sparsity in the discrete mechanics and optimal control method with application to human motion planning (SB, JSC, BL), pp. 769–774.

ICALP-v1-2015-Sanyal #bound #fourier

Near-Optimal Upper Bound on Fourier Dimension of Boolean Functions in Terms of Fourier Sparsity (SS), pp. 1035–1045.

ICML-2015-HegdeIS #framework

A Nearly-Linear Time Framework for Graph-Structured Sparsity (CH, PI, LS), pp. 928–937.

CIKM-2014-TaoIWS #analysis #canonical #correlation

Exploring Shared Subspace and Joint Sparsity for Canonical Correlation Analysis (LT, HHSI, YW, XS), pp. 1887–1890.

CIKM-2014-XuLLZCS #mining

Latent Aspect Mining via Exploring Sparsity and Intrinsic Information (YX, TL, WL, ZZ, HC, AMCS), pp. 879–888.

CIKM-2014-ZhangZZLM #comprehension #constraints #matrix

Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables (YZ, MZ, YZ, YL, SM), pp. 1189–1198.

ICML-c2-2014-YuanLZ #optimisation

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization (XY, PL, TZ), pp. 127–135.

ICPR-2014-ChenH #composition #detection

Implicit Rank-Sparsity Decomposition: Applications to Saliency/Co-saliency Detection (YLC, CTH), pp. 2305–2310.

ICPR-2014-ChenPH #algorithm

O(1) Algorithms for Overlapping Group Sparsity (CC, ZP, JH), pp. 1645–1650.

ICPR-2014-LiuL0L #classification #image #learning

Regularized Hierarchical Feature Learning with Non-negative Sparsity and Selectivity for Image Classification (BL, JL, XB, HL), pp. 4293–4298.

ICPR-2014-LiuS #analysis

Discriminative Partition Sparsity Analysis (LL, LS), pp. 1597–1602.

ICPR-2014-WangYWWL

Discriminative Representative Selection via Structure Sparsity (BW, QY, SW, LW, GL), pp. 1401–1406.

STOC-2013-ClarksonW #approximate #rank

Low rank approximation and regression in input sparsity time (KLC, DPW), pp. 81–90.

STOC-2013-MengM #linear #robust

Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression (XM, MWM), pp. 91–100.

STOC-2013-NelsonN #bound

Sparsity lower bounds for dimensionality reducing maps (JN, HLN), pp. 101–110.

ICEIS-v2-2013-DjuanaXLJC #ontology #problem #recommendation

An Ontology-based Method for Sparsity Problem in Tag Recommendation (ED, YX, YL, AJ, CC), pp. 467–474.

CIKM-2013-AlbakourMO #effectiveness #microblog #on the #realtime

On sparsity and drift for effective real-time filtering in microblogs (MDA, CM, IO), pp. 419–428.

ICML-c1-2013-MehtaG #bound #predict

Sparsity-Based Generalization Bounds for Predictive Sparse Coding (NAM, AGG), pp. 36–44.

ICML-c1-2013-WongAF #adaptation #modelling #visual notation

Adaptive Sparsity in Gaussian Graphical Models (EW, SPA, PTF), pp. 311–319.

ICML-c3-2013-DalalyanHMS #learning #modelling #programming

Learning Heteroscedastic Models by Convex Programming under Group Sparsity (ASD, MH, KM, JS), pp. 379–387.

ICML-c3-2013-Lopes

Estimating Unknown Sparsity in Compressed Sensing (ML), pp. 217–225.

ICML-c3-2013-WangNH13a #clustering #learning #multi

Multi-View Clustering and Feature Learning via Structured Sparsity (HW, FN, HH), pp. 352–360.

ICML-c3-2013-ZhangYJLH #bound #kernel #learning #online

Online Kernel Learning with a Near Optimal Sparsity Bound (LZ, JY, RJ, ML, XH), pp. 621–629.

RecSys-2013-Guo #recommendation #similarity #trust

Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems (GG), pp. 451–454.

SAC-2013-BlondelSU #classification #constraints #learning #using

Learning non-linear classifiers with a sparsity constraint using L1 regularization (MB, KS, KU), pp. 167–169.

ICALP-v1-2012-ItoTY #algorithm

Constant-Time Algorithms for Sparsity Matroids (HI, SiT, YY), pp. 498–509.

ICPR-2012-AryafarJS #automation #classification #using

Automatic musical genre classification using sparsity-eager support vector machines (KA, SJ, AS), pp. 1526–1529.

ICPR-2012-LiuSZ #feature model #graph

Sparsity Score: A new filter feature selection method based on graph (ML, DS, DZ), pp. 959–962.

KDD-2012-JiZL #clustering

A sparsity-inducing formulation for evolutionary co-clustering (SJ, WZ, JL), pp. 334–342.

KDD-2012-ZhangL #component #constraints #mining #rank #recognition

Mining discriminative components with low-rank and sparsity constraints for face recognition (QZ, BL), pp. 1469–1477.

RecSys-2012-ZelenikB #information management #recommendation

Reducing the sparsity of contextual information for recommender systems (DZ, MB), pp. 341–344.

CIKM-2011-NaveedGKA #documentation #microblog #quality

Searching microblogs: coping with sparsity and document quality (NN, TG, JK, ACA), pp. 183–188.

ICML-2011-VirtanenKK

Bayesian CCA via Group Sparsity (SV, AK, SK), pp. 457–464.

SIGIR-2011-MoshfeghiPJ #collaboration #semantics #using

Handling data sparsity in collaborative filtering using emotion and semantic based features (YM, BP, JMJ), pp. 625–634.

ICML-2010-KimX #multi

Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (SK, EPX), pp. 543–550.

ICPR-2010-ErdoganG #using

Semi-blind Speech-Music Separation Using Sparsity and Continuity Priors (HE, EMG), pp. 4573–4576.

ICPR-2010-LiuS #similarity #using #visual notation

Visual Tracking Using Sparsity Induced Similarity (HL, FS), pp. 1702–1705.

ICALP-v1-2009-GopalanOSSW #fourier #testing

Testing Fourier Dimensionality and Sparsity (PG, RO, RAS, AS, KW), pp. 500–512.

ICML-2009-DuchiS

Boosting with structural sparsity (JCD, YS), pp. 297–304.

ICML-2009-HuangZM #learning

Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.

ICML-2009-ZhuX #markov #network #on the

On primal and dual sparsity of Markov networks (JZ, EPX), pp. 1265–1272.

DAC-2008-DargaSM #performance #symmetry #using

Faster symmetry discovery using sparsity of symmetries (PTD, KAS, ILM), pp. 149–154.

RecSys-2008-YildirimK #collaboration #problem #random

A random walk method for alleviating the sparsity problem in collaborative filtering (HY, MSK), pp. 131–138.

SAS-2005-SimonK #analysis

Exploiting Sparsity in Polyhedral Analysis (AS, AK), pp. 336–351.

KDD-2005-Kolcz #classification #naive bayes

Local sparsity control for naive Bayes with extreme misclassification costs (AK), pp. 128–137.

STOC-1996-KapurS

Sparsity Considerations in Dixon Resultants (DK, TS), pp. 184–191.