Stem princip$ (all stems)

84 papers:

SIGMOD-2015-ElgamalYAMH #analysis #big data #component #distributed #named #scalability

sPCA: Scalable Principal Component Analysis for Big Data on Distributed Platforms (TE, MY, AA, WM, MH), pp. 79–91.

DUXU-IXD-2015-IslamI #comprehension #interface #ontology #semantics #set #web

Understanding the Semantics of Web Interface Signs: A Set of Ontological Principals (MNI, AKMNI), pp. 46–53.

ICML-2015-YangX15a #analysis #component #streaming

Streaming Sparse Principal Component Analysis (WY, HX), pp. 494–503.

MLDM-2015-KarkkainenS #analysis #component #robust

Robust Principal Component Analysis of Data with Missing Values (TK, MS), pp. 140–154.

POPL-2015-GarciaC #source code

Principal Type Schemes for Gradual Programs (RG, MC), pp. 303–315.

STOC-2014-DworkTT0 #analysis #bound #component #privacy

Analyze gauss: optimal bounds for privacy-preserving principal component analysis (CD, KT, AT, LZ), pp. 11–20.

CIKM-2014-ShangLCC #analysis #component #robust

Robust Principal Component Analysis with Missing Data (FS, YL, JC, HC), pp. 1149–1158.

ICML-c2-2014-NieYH #analysis #component #robust

Optimal Mean Robust Principal Component Analysis (FN, JY, HH), pp. 1062–1070.

ICML-c2-2014-ZhaoMXZZ #analysis #component #robust

Robust Principal Component Analysis with Complex Noise (QZ, DM, ZX, WZ, LZ), pp. 55–63.

ICPR-2014-YiS #recognition #representation

Principal Local Binary Patterns for Face Representation and Recognition (JY, FS), pp. 4554–4559.

ICML-c1-2013-HanL13a #analysis #component

Principal Component Analysis on non-Gaussian Dependent Data (FH, HL), pp. 240–248.

ICML-c3-2013-Kuleshov #algorithm #analysis #component #performance

Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration (VK), pp. 1418–1425.

KDD-2013-SunXY #analysis #component #robust

Robust principal component analysis via capped norms (QS, SX, JY), pp. 311–319.

CHI-2012-BaurBB #analysis #component #music #scalability

Listening factors: a large-scale principal components analysis of long-term music listening histories (DB, JB, AB), pp. 1273–1276.

ICML-2012-McCartin-LimMW #approximate

Approximate Principal Direction Trees (MML, AM, RW), p. 209.

ICPR-2012-Abdel-HakimE #analysis #component #named #online #performance #robust

FRPCA: Fast Robust Principal Component Analysis for online observations (AEAH, MES), pp. 413–416.

ICPR-2012-WangSCPZ #analysis #component #feature model #named

STPCA: Sparse tensor Principal Component Analysis for feature extraction (SW, MS, YHC, EPP, CZ), pp. 2278–2281.

ESOP-2011-HuntS #exponential #polynomial #security #type system

From Exponential to Polynomial-Time Security Typing via Principal Types (SH, DS), pp. 297–316.

ICEIS-v3-2011-QuG #analysis #identification #outsourcing #risk management

The Analysis of IT Outsourcing Risk Identification on Principal-agent Theory (LQ, ZG), pp. 488–491.

SOSP-2011-MaoCZWZK #api #fault #multi

Software fault isolation with API integrity and multi-principal modules (YM, HC, DZ, XW, NZ, MFK), pp. 115–128.

SAS-2010-AmatoPS #abstract domain #analysis #component

Deriving Numerical Abstract Domains via Principal Component Analysis (GA, MP, FS), pp. 134–150.

CIKM-2010-MinZWM #component #keyword #topic

Decomposing background topics from keywords by principal component pursuit (KM, ZZ, JW, YM), pp. 269–278.

ICPR-2010-DurakN #classification #detection #image

Principal Contour Extraction and Contour Classification to Detect Coronal Loops from the Solar Images (ND, ON), pp. 2403–2406.

ICPR-2010-FengLYL #online #video

Online Principal Background Selection for Video Synopsis (SF, SL, ZYY, SZL), pp. 17–20.

ICPR-2010-PaivaT #detection #image #using

Detection of Salient Image Points Using Principal Subspace Manifold Structure (ARCP, TT), pp. 1389–1392.

ICPR-2010-QiaoC #3d #analysis #component #modelling #statistics #using

Statistical Texture Modeling for Medical Volume Using Generalized N-Dimensional Principal Component Analysis Method and 3D Volume Morphing (XQ, YWC), pp. 2488–2491.

SAC-2010-TeixeiraM #data type #detection

Data stream anomaly detection through principal subspace tracking (PHdST, RLM), pp. 1609–1616.

ESOP-2009-MostrousYH #commutative #type system

Global Principal Typing in Partially Commutative Asynchronous Sessions (DM, NY, KH), pp. 316–332.

ICML-2009-BaillyDR #analysis #component #grammar inference #problem

Grammatical inference as a principal component analysis problem (RB, FD, LR), pp. 33–40.

CASE-2008-Chirikjian

Parts entropy and the principal kinematic formula (GSC), pp. 864–869.

ICML-2008-LuPV #analysis #component #multi

Uncorrelated multilinear principal component analysis through successive variance maximization (HL, KNP, ANV), pp. 616–623.

KDD-2008-BoutsidisMD #analysis #component #feature model

Unsupervised feature selection for principal components analysis (CB, MWM, PD), pp. 61–69.

ESOP-2007-DreyerB #composition #source code

Principal Type Schemes for Modular Programs (DD, MB), pp. 441–457.

ICML-2007-dAspremontBG #analysis #component

Full regularization path for sparse principal component analysis (Ad, FRB, LEG), pp. 177–184.

CASE-2006-ShaoHM #algorithm #analysis #component #linear #multi #using

Quantitative Analysis Using NIR by Building Principal Component- Multiple Linear Regression-BP Algorithm (YS, YH, JM), pp. 161–164.

ICML-2006-ConitzerG #algorithm #learning #online #problem

Learning algorithms for online principal-agent problems (and selling goods online) (VC, NG), pp. 209–216.

ICML-2006-DingZHZ #analysis #component #invariant #named #robust

R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization (CHQD, DZ, XH, HZ), pp. 281–288.

ICPR-v1-2006-ZhangDDM #multi #recognition

A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions (WZ, HD, TGD, ENM), pp. 778–782.

ICPR-v2-2006-LuPV #analysis #component #multi #recognition

Multilinear Principal Component Analysis of Tensor Objects for Recognition (HL, KNP, ANV), pp. 776–779.

ICPR-v2-2006-SanguansatAJM #2d #analysis #component #recognition

Class-Specific Subspace-Based Two-Dimensional Principal Component Analysis for Face Recognition (PS, WA, SJ, SM), pp. 1246–1249.

ICPR-v2-2006-ZhengL #analysis #component #kernel #learning #locality #problem

Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis (WSZ, JHL), pp. 456–459.

ICPR-v3-2006-LiuW #algorithm #analysis #component #feature model #recognition #search-based

Feature Extraction with Genetic Algorithms Based Nonlinear Principal Component Analysis for Face Recognition (NL, HW), pp. 461–464.

ICPR-v3-2006-TangkuampienS #analysis #component #kernel

Human Motion De-noising via Greedy Kernel Principal Component Analysis Filtering (TT, DS), pp. 457–460.

ICPR-v3-2006-XuanCZYHSF #analysis #classification #component #novel

A Novel Pattern Classification Scheme: Classwise Non-Principal Component Analysis (CNPCA) (GX, PC, XZ, QY, CH, YQS, DF), pp. 320–323.

KDD-2006-YuYTKW #analysis #component #probability

Supervised probabilistic principal component analysis (SY, KY, VT, HPK, MW), pp. 464–473.

ICFP-2005-MakholmW #mixin #morphism #type inference

Type inference, principal typings, and let-polymorphism for first-class mixin modules (HM, JBW), pp. 156–167.

ICML-2004-DingH04a #analysis #clustering #component

K-means clustering via principal component analysis (CHQD, XH).

ICML-2004-SuD #automation #component #probability

Automated hierarchical mixtures of probabilistic principal component analyzers (TS, JGD).

ICPR-v1-2004-ChenCL #algorithm #detection #robust

A Robust Algorithm of Principal Curve Detection (MC, ZC, YL), pp. 429–432.

ICPR-v1-2004-VaswaniC #analysis #classification #component #null #probability

Classification Probability Analysis of Principal Component Null Space Analysis (NV, RC), pp. 240–243.

ICPR-v2-2004-DeepuMR #analysis #component #online #recognition

Principal Component Analysis for Online Handwritten Character Recognition (VD, SM, AGR), pp. 327–330.

ICPR-v2-2004-ImiyaY #analysis

Principal Curve Analysis for Temporal Data (AI, NY), pp. 475–478.

ICPR-v3-2004-BakerB

Principal Flow for Tubular Objects with Non-Circular Cross-Sections (GB, NB), pp. 750–753.

ICPR-v4-2004-MacDormanCA #component #generative #network #segmentation

Periodic Nonlinear Principal Component Neural Networks for Humanoid Motion Segmentation, Generalization, and Generation (KFM, RC, MA), pp. 537–540.

ICPR-v4-2004-ViguerasBS #multi #on the

On the Influence of Fixing the Principal Point in Frame-by-Frame Multiplanar Calibration (JFV, MOB, GS), pp. 120–123.

POPL-2004-AnconaZ #java

Principal typings for Java-like languages (DA, EZ), pp. 306–317.

MLDM-2003-TanakaU #analysis #component #multi #principle #using

Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle (YT, KU), pp. 252–265.

TLCA-2003-CoppolaR #logic #type system

Principal Typing in Elementary Affine Logic (PC, SRDR), pp. 90–104.

ASIA-PEPM-2002-LopezH

Principal type specialisation (PEML, JH), pp. 94–105.

STOC-2002-Hallgren #algorithm #equation #polynomial #problem #quantum

Polynomial-time quantum algorithms for Pell’s equation and the principal ideal problem (SH), pp. 653–658.

ICALP-2002-Wells

The Essence of Principal Typings (JBW), pp. 913–925.

CHI-2002-GrossmanBKFKB #3d

Creating principal 3D curves with digital tape drawing (TG, RB, GK, GWF, AK, WB), pp. 121–128.

ICPR-v2-2002-LuoWH #component #graph #independence

The Independent and Principal Component of Graph Spectra (BL, RCW, ERH), pp. 164–167.

ICPR-v2-2002-RuizLG

A Note on Principal Point Estimability (AR, PELdT, GGM), pp. 304–307.

ICPR-v2-2000-PerantonisPV #analysis #classification #component #paradigm #using

Supervised Principal Component Analysis Using a Smooth Classifier Paradigm (SJP, SP, VV), pp. 2109–2112.

ICPR-v3-2000-FukuiIIW #analysis #component #using

Sign of Gaussian Curvature from Eigen Plane Using Principal Components Analysis (SF, YI, AI, RJW), pp. 3807–3810.

ICPR-v3-2000-KeglK #linear #using

Piecewise Linear Skeletonization Using Principal Curves (BK, AK), pp. 3135–3138.

ICDAR-1999-DehkordiSW #approach #classification #component #word

A Principal Component Approach to Classification of Handwritten Words (MED, NS, RJW), pp. 781–784.

ICDAR-1999-SteinherzIR #analysis #component #detection

Skew Detection via Principal Components Analysis (TS, NI, ER), pp. 153–156.

FM-v1-1999-SyversonS #formal method

Group Principals and the Formalization of Anonymity (PFS, SGS), pp. 814–833.

ICFP-1999-ZdancewicGM #programming language #proving

Principals in Programming Languages: A Syntactic Proof Technique (SZ, DG, JGM), pp. 197–207.

POPL-1999-KfouryW #decidability #type inference

Principality and Decidable Type Inference for Finite-Rank Intersection Types (AJK, JBW), pp. 161–174.

TLCA-1999-BrodaD

Counting a Type’s Principal Inhabitants (SB, LD), pp. 69–82.

ICDAR-1997-KawataniS #classification #component #design #difference #using

Complementary Classifier Design Using Difference Principal Components (TK, HS), pp. 875–880.

ICML-1997-Opitz #approach #component #effectiveness #network

The Effective Size of a Neural Network: A Principal Component Approach (DWO), pp. 263–271.

ESEC-FSE-1997-ForgacsB #slicing

Feasible Test Path Selection by Principal Slicing (IF, AB), pp. 378–394.

ICPR-1996-Hachimura #retrieval #using

Retrieval of paintings using principal color information (KH), pp. 130–134.

ICPR-1996-TanakaI96a #recognition #using

Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition (HTT, MI), pp. 638–642.

POPL-1996-Jim #question #what

What Are Principal Typings and What Are They Good For? (TJ), pp. 42–53.

ML-1992-BalaMW #induction

The Principal Axes Method for Constructive Induction (JWB, RSM, JW), pp. 20–29.

POPL-1992-Tofte #higher-order

Principal Signatures for Higher-Order Program Modules (MT), pp. 189–199.

ICALP-1984-Ferment #matrix #product line

Principality Results About Some Matrix Languages Families (DF), pp. 151–161.

POPL-1982-DamasM #functional #source code

Principal Type-Schemes for Functional Programs (LD, RM), pp. 207–212.

STOC-1973-Greibach #context-free grammar #polynomial

Jump PDA’s, Deterministic Context-Free Languages Principal AFDLs and Polynomial Time Recognition-Extended Abstract (SAG), pp. 20–28.