Stem spectral$ (all stems)

174 papers:

STOC-2015-ZhuLO #matrix #multi

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates (ZAZ, ZL, LO), pp. 237–245.

ICML-2015-BoutsidisKG #clustering

Spectral Clustering via the Power Method — Provably (CB, PK, AG), pp. 40–48.

ICML-2015-ChenS #rank

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons (YC, CS), pp. 371–380.

ICML-2015-GhoshdastidarD #clustering

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning (DG, AD), pp. 400–409.

ICML-2015-Yang0JZ #bound #fault #set

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection (TY, LZ, RJ, SZ), pp. 135–143.

KDD-2015-GleichM #algorithm #graph #learning #using

Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.

KDD-2015-LiuLWTF #clustering

Spectral Ensemble Clustering (HL, TL, JW, DT, YF), pp. 715–724.

DATE-2014-KarakonstantisSSAB #analysis #approach #energy #variability

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability (GK, AS, MMS, DA, AB), pp. 1–6.

DRR-2014-LiPLD #analysis #online #verification

On-line signature verification method by Laplacian spectral analysis and dynamic time warping (CL, LP, CL, XD), p. ?–10.

DLT-2014-Sinya #automaton #finite #graph

Graph Spectral Properties of Deterministic Finite Automata — (Short Paper) (RS), pp. 76–83.

ICML-c1-2014-DenisGH #bound #learning #matrix

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning (FD, MG, AH), pp. 449–457.

ICML-c2-2014-GleichM #algorithm #approximate #case study

Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow (DG, MWM), pp. 1018–1025.

ICML-c2-2014-QuattoniBCG #sequence

Spectral Regularization for Max-Margin Sequence Tagging (AQ, BB, XC, AG), pp. 1710–1718.

ICML-c2-2014-ValkoMKK #graph

Spectral Bandits for Smooth Graph Functions (MV, RM, BK, TK), pp. 46–54.

ICPR-2014-BruneauPO #algorithm #automation #clustering #heuristic

A Heuristic for the Automatic Parametrization of the Spectral Clustering Algorithm (PB, OP, BO), pp. 1313–1318.

ICPR-2014-GuoZLCZ #clustering #kernel #learning #multi

Multiple Kernel Learning Based Multi-view Spectral Clustering (DG, JZ, XL, YC, CZ), pp. 3774–3779.

ICPR-2014-HiraiOHT

An HDR Spectral Imaging System for Time-Varying Omnidirectional Scene (KH, NO, TH, ST), pp. 2059–2064.

ICPR-2014-HuynhR #image

Recovery of Spectral Sensitivity Functions from a Colour Chart Image under Unknown Spectrally Smooth Illumination (CPH, ARK), pp. 708–713.

ICPR-2014-IoannidisCL #clustering #modelling #multi #using

Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering (AI, VC, AL), pp. 3463–3468.

ICPR-2014-JenzriFG #robust

Robust Context Dependent Spectral Unmixing (HJ, HF, PDG), pp. 643–647.

ICPR-2014-KacheleZMS #feature model #quality #recognition #using

Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition (MK, DZ, SM, FS), pp. 803–808.

ICPR-2014-LefevreAG #clustering

Brain Lobes Revealed by Spectral Clustering (JL, GA, DG), pp. 562–567.

ICPR-2014-NocetiO #classification #graph #kernel #process

A Spectral Graph Kernel and Its Application to Collective Activities Classification (NN, FO), pp. 3892–3897.

ICPR-2014-Ozay #image #multi #segmentation

Semi-supervised Segmentation Fusion of Multi-spectral and Aerial Images (MO), pp. 3839–3844.

ICPR-2014-TasdemirMY #approximate #clustering

Geodesic Based Similarities for Approximate Spectral Clustering (KT, YM, IY), pp. 1360–1364.

KDD-2014-WangZQZ #using

Improving the modified nyström method using spectral shifting (SW, CZ, HQ, ZZ), pp. 611–620.

SAC-2014-QuilleTR #analysis

Spectral analysis and text processing over the computer science literature: patterns and discoveries (RVEQ, CTJ, JFRJ), pp. 653–657.

DATE-2013-TodorovMRS #approach #clustering #synthesis

A spectral clustering approach to application-specific network-on-chip synthesis (VT, DMG, HR, US), pp. 1783–1788.

STOC-2013-KwokLLGT #algorithm #analysis #clustering #difference #higher-order

Improved Cheeger’s inequality: analysis of spectral partitioning algorithms through higher order spectral gap (TCK, LCL, YTL, SOG, LT), pp. 11–20.

STOC-2013-Miller #graph #optimisation #problem #scalability #using

Solving large optimization problems using spectral graph theory (GLM), p. 981.

DLT-2013-Jungers

Joint Spectral Characteristics: A Tale of Three Disciplines (RMJ), pp. 27–28.

HCI-III-2013-YangWC #clustering #fuzzy #image #kernel #segmentation #similarity

Kernel Fuzzy Similarity Measure-Based Spectral Clustering for Image Segmentation (YY, YW, YmC), pp. 246–253.

ICML-c1-2013-BootsG #approach #learning

A Spectral Learning Approach to Range-Only SLAM (BB, GJG), pp. 19–26.

ICML-c3-2013-ChagantyL #linear

Spectral Experts for Estimating Mixtures of Linear Regressions (ATC, PL), pp. 1040–1048.

ICML-c3-2013-ChenC #matrix

Spectral Compressed Sensing via Structured Matrix Completion (YC, YC), pp. 414–422.

ICML-c3-2013-HuangS #learning #markov #modelling

Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.

STOC-2012-LeeGT #clustering #higher-order #multi

Multi-way spectral partitioning and higher-order cheeger inequalities (JRL, SOG, LT), pp. 1117–1130.

STOC-2012-OrecchiaSV #algorithm #approximate #exponential

Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator (LO, SS, NKV), pp. 1141–1160.

CIKM-2012-ShangJLW #learning

Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.

ICML-2012-BalleQC #learning #modelling #optimisation

Local Loss Optimization in Operator Models: A New Insight into Spectral Learning (BB, AQ, XC), p. 236.

ICML-2012-DhillonRFU #modelling #using #word

Using CCA to improve CCA: A new spectral method for estimating vector models of words (PSD, JR, DPF, LHU), p. 13.

ICPR-2012-FuLBF

Spectral correspondence method for fingerprint minutia matching (XF, CL, JB, JF), pp. 1743–1746.

ICPR-2012-GuiST #analysis #estimation #parametricity

Regularization parameter estimation for spectral regression discriminant analysis based on perturbation theory (JG, ZS, TT), pp. 401–404.

ICPR-2012-HuZFZ #clustering #multi #strict

Multi-way constrained spectral clustering by nonnegative restriction (HH, JZ, JF, JZ), pp. 1550–1553.

ICPR-2012-LamSS #image #statistics #using

Denoising hyperspectral images using spectral domain statistics (AL, IS, YS), pp. 477–480.

ICPR-2012-MitraKGSMLOVM #clustering #multimodal #performance

Spectral clustering to model deformations for fast multimodal prostate registration (JM, ZK, SG, DS, RM, XL, AO, JCV, FM), pp. 2622–2625.

ICPR-2012-PanS #3d

3D shape isometric correspondence by spectral assignment (XP, LGS), pp. 2210–2213.

ICPR-2012-RatnasingamR #recognition #representation

A spectral reflectance representation for recognition and reproduction (SR, ARK), pp. 1900–1903.

ICPR-2012-ZhangH #feature model #recognition #using

Face recognition using semi-supervised spectral feature selection (ZZ, ERH), pp. 1294–1297.

ICPR-2012-ZhangH12a #feature model #recognition

Unsupervised spectral feature selection for face recognition (ZZ, ERH), pp. 1787–1790.

KDD-2012-CorreaL #clustering #graph #using

Locally-scaled spectral clustering using empty region graphs (CDC, PL), pp. 1330–1338.

KDD-2012-WauthierJJ #clustering #nondeterminism #reduction

Active spectral clustering via iterative uncertainty reduction (FLW, NJ, MIJ), pp. 1339–1347.

KDIR-2012-VolkovichA #clustering

Model Selection and Stability in Spectral Clustering (ZV, RA), pp. 25–34.

HPDC-2012-HefeedaGA #approximate #clustering #dataset #distributed #scalability

Distributed approximate spectral clustering for large-scale datasets (MH, FG, WAA), pp. 223–234.

ICST-2012-MalikK #analysis #graph #using

Dynamic Shape Analysis Using Spectral Graph Properties (MZM, SK), pp. 211–220.

HCI-MIIE-2011-MinKH #recognition #using

Spectral Subtraction Based Emotion Recognition Using EEG (JHM, HOK, KSH), pp. 569–576.

CIKM-2011-KimKFL #analysis

Spectral analysis of a blogosphere (SWK, KNK, CF, JHL), pp. 2145–2148.

ICML-2011-DasK #algorithm #approximate #set #taxonomy

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection (AD, DK), pp. 1057–1064.

ICML-2011-KumarD #approach #clustering #multi

A Co-training Approach for Multi-view Spectral Clustering (AK, HDI), pp. 393–400.

ICML-2011-ParikhSX #algorithm #modelling #visual notation

A Spectral Algorithm for Latent Tree Graphical Models (APP, LS, EPX), pp. 1065–1072.

KDIR-2011-AzamV #clustering #comparative #evaluation #metric #proximity

A Comparative Evaluation of Proximity Measures for Spectral Clustering (NFA, HLV), pp. 30–41.

STOC-2010-Kannan #matrix

Spectral methods for matrices and tensors (RK), pp. 1–12.

STOC-2010-RaghavendraST #approximate #graph #parametricity

Approximations for the isoperimetric and spectral profile of graphs and related parameters (PR, DS, PT), pp. 631–640.

CIKM-2010-FangSS #clustering #learning #multi

Multilevel manifold learning with application to spectral clustering (HrF, SS, YS), pp. 419–428.

CIKM-2010-KunegisFB #evolution #network

Network growth and the spectral evolution model (JK, DF, CB), pp. 739–748.

ICML-2010-BshoutyL #clustering #linear #using

Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering (NHB, PML), pp. 135–142.

ICML-2010-NiuDJ #clustering #multi

Multiple Non-Redundant Spectral Clustering Views (DN, JGD, MIJ), pp. 831–838.

ICPR-2010-BatesLM #representation

Scale-Space Spectral Representation of Shape (JB, XL, WM), pp. 2648–2651.

ICPR-2010-BourlaiKRCH #verification

Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band (TB, NDK, AR, BC, LH), pp. 1343–1347.

ICPR-2010-BozkurtEEE #recognition #speech

Use of Line Spectral Frequencies for Emotion Recognition from Speech (EB, EE, ÇEE, ATE), pp. 3708–3711.

ICPR-2010-IbrahimTH #image #invariant #representation

Spectral Invariant Representation for Spectral Reflectance Image (AI, ST, TH), pp. 2776–2779.

ICPR-2010-LettnerS #robust

Combining Spectral and Spatial Features for Robust Foreground-Background Separation (ML, RS), pp. 1969–1972.

ICPR-2010-ManciniFZ #detection #multi

Road Change Detection from Multi-Spectral Aerial Data (AM, EF, PZ), pp. 448–451.

ICPR-2010-SfikasHN #analysis #clustering #multi #using

Multiple Atlas Inference and Population Analysis Using Spectral Clustering (GS, CH, CN), pp. 2500–2503.

ICPR-2010-SmeetsFHVS #3d #approach #composition #invariant #modelling #recognition #using

Fusion of an Isometric Deformation Modeling Approach Using Spectral Decomposition and a Region-Based Approach Using ICP for Expression-Invariant 3D Face Recognition (DS, TF, JH, DV, PS), pp. 1172–1175.

ICPR-2010-TekeT #image #multi #strict #using

Multi-spectral Satellite Image Registration Using Scale-Restricted SURF (MT, AT), pp. 2310–2313.

ICPR-2010-XuV

Binary Representations of Fingerprint Spectral Minutiae Features (HX, RNJV), pp. 1212–1216.

KDD-2010-WangD #clustering #flexibility

Flexible constrained spectral clustering (XW, ID), pp. 563–572.

DAC-2009-CochranR

Spectral techniques for high-resolution thermal characterization with limited sensor data (RC, SR), pp. 478–483.

ICDAR-2009-LettnerS #documentation #image #multi #segmentation

Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents (ML, RS), pp. 813–817.

ICML-2009-BuhlerH #clustering #graph

Spectral clustering based on the graph p-Laplacian (TB, MH), pp. 81–88.

ICML-2009-KumarMT #approximate #composition #on the

On sampling-based approximate spectral decomposition (SK, MM, AT), pp. 553–560.

ICML-2009-KunegisL #graph transformation #learning #predict

Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.

KDD-2009-YanHJ #approximate #clustering #performance

Fast approximate spectral clustering (DY, LH, MIJ), pp. 907–916.

MLDM-2009-SakaiI #clustering #performance #random

Fast Spectral Clustering with Random Projection and Sampling (TS, AI), pp. 372–384.

CASE-2008-SunEHMMMBLM #analysis #automation #biology #integration #multi #user interface

Integration of user interface, device control, data acquisition and analysis for automated multi-spectral imaging of single biological cells (CSS, JRE, MH, TWM, SKM, SM, LWB, MEL, DRM), pp. 1013–1018.

DATE-2008-TcheghoMS

Optimal High-Resolution Spectral Analyzer (AT, HM, SS), pp. 62–67.

ICML-2008-ColemanSW #clustering #consistency

Spectral clustering with inconsistent advice (TC, JS, AW), pp. 152–159.

ICPR-2008-AndriyashinPJ #dependence

Illuminant dependence of PCA, NMF and NTF in spectral color imaging (AA, JP, TJ), pp. 1–4.

ICPR-2008-LezorayTE #clustering #graph

Impulse noise removal by spectral clustering and regularization on graphs (OL, VTT, AE), pp. 1–4.

ICPR-2008-LiuDJM #3d #kernel #robust

Kernel functions for robust 3D surface registration with spectral embeddings (XL, AD, MJ, WM), pp. 1–4.

ICPR-2008-LiZWH #analysis #clustering #multi

Multiclass spectral clustering based on discriminant analysis (XL, ZZ, YW, WH), pp. 1–4.

ICPR-2008-XiaoWH #graph #invariant #recognition #using

Object recognition using graph spectral invariants (XB, RCW, ERH), pp. 1–4.

KDD-2008-LingDXYY #learning

Spectral domain-transfer learning (XL, WD, GRX, QY, YY), pp. 488–496.

KDD-2008-SunJY #classification #learning #multi

Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.

SIGIR-2008-LiuLLJ #clustering #geometry #query #ranking

Spectral geometry for simultaneously clustering and ranking query search results (YL, WL, YL, LJ), pp. 539–546.

DATE-2007-ZhuZCXZ #grid #probability #process

A sparse grid based spectral stochastic collocation method for variations-aware capacitance extraction of interconnects under nanometer process technology (HZ, XZ, WC, JX, DZ), pp. 1514–1519.

ICDAR-2007-PapavassiliouSKC #parametricity #verification

A Parametric Spectral-Based Method for Verification of Text in Videos (VP, TS, VK, GC), pp. 879–883.

CIKM-2007-CaiHZH #locality

Regularized locality preserving indexing via spectral regression (DC, XH, WVZ, JH), pp. 741–750.

ECIR-2007-LiuYZQM #clustering #optimisation #performance #scalability

Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization (TYL, HYY, XZ, TQ, WYM), pp. 319–330.

ICML-2007-LiYW #distance #framework #learning #metric #reduction

A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.

ICML-2007-TomiokaA #matrix

Classifying matrices with a spectral regularization (RT, KA), pp. 895–902.

ICML-2007-ZhaoL #feature model #learning

Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.

ICML-2007-ZhouB #clustering #learning #multi

Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.

KDD-2007-ChiSZHT #clustering

Evolutionary spectral clustering by incorporating temporal smoothness (YC, XS, DZ, KH, BLT), pp. 153–162.

KDD-2007-ShigaTM #approach #clustering #composition #network

A spectral clustering approach to optimally combining numericalvectors with a modular network (MS, IT, HM), pp. 647–656.

ICALP-v1-2006-Coja-Oghlan #adaptation #clustering #graph #heuristic #random

An Adaptive Spectral Heuristic for Partitioning Random Graphs (ACO), pp. 691–702.

ICALP-v1-2006-Coja-OghlanL #graph #random

The Spectral Gap of Random Graphs with Given Expected Degrees (ACO, AL), pp. 15–26.

ICML-2006-LongZWY #clustering #multi #relational

Spectral clustering for multi-type relational data (BL, Z(Z, XW, PSY), pp. 585–592.

ICML-2006-MoghaddamWA #bound

Generalized spectral bounds for sparse LDA (BM, YW, SA), pp. 641–648.

ICML-2006-XiaoSB #reduction

A duality view of spectral methods for dimensionality reduction (LX, JS, SPB), pp. 1041–1048.

ICML-2006-ZhangK #kernel #matrix #performance

Block-quantized kernel matrix for fast spectral embedding (KZ, JTK), pp. 1097–1104.

ICPR-v1-2006-FuTC #orthogonal #using

Specular Free Spectral Imaging Using Orthogonal Subspace Projection (ZF, RTT, TC), pp. 812–815.

ICPR-v2-2006-ScarpaH #clustering #independence #segmentation

Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering (GS, MH), pp. 151–154.

ICPR-v4-2006-WhiteW #graph

Mixing spectral representations of graphs (DHW, RCW), pp. 140–144.

SAC-2006-GuoW #data mining #mining #on the #privacy

On the use of spectral filtering for privacy preserving data mining (SG, XW), pp. 622–626.

STOC-2005-Vu #matrix #random

Spectral norm of random matrices (VHV), pp. 423–430.

ICML-2005-ShaS #analysis #reduction

Analysis and extension of spectral methods for nonlinear dimensionality reduction (FS, LKS), pp. 784–791.

SIGIR-2005-BastM #retrieval #why

Why spectral retrieval works (HB, DM), pp. 11–18.

STOC-2004-Kelner #bound #clustering #graph

Spectral partitioning, eigenvalue bounds, and circle packings for graphs of bounded genus (JAK), pp. 455–464.

STOC-2004-KempeM #algorithm #analysis #distributed

A decentralized algorithm for spectral analysis (DK, FM), pp. 561–568.

ICML-2004-DingH #clustering

Linearized cluster assignment via spectral ordering (CHQD, XH).

ICPR-v1-2004-ChabrierELRM #evaluation #image #multi #segmentation

Unsupervised Evaluation of Image Segmentation Application to Multi-spectral Images (SC, BE, HL, CR, PM), pp. 576–579.

ICPR-v1-2004-KalenovaTB #image

Spectral Image Distortion Map (DK, PJT, VB), pp. 668–671.

ICPR-v2-2004-CostaGB

Spectral Characterization of Orientation Data along Curvilinear Structures (JPDC, CG, PB), pp. 517–520.

ICPR-v2-2004-DroriFY

Spectral Sound Gap Filling (ID, AF, YY), pp. 871–874.

ICPR-v2-2004-LindgrenH #component #image #independence #learning #representation

Learning High-level Independent Components of Images through a Spectral Representation (JTL, AH), pp. 72–75.

ICPR-v2-2004-PranckevicieneBS #classification #identification

Consensus-Based Identification of Spectral Signatures for Classification of High-Dimensional Biomedical Spectra (EP, RB, RLS), pp. 319–322.

ICPR-v2-2004-Srinivasan #approximate #segmentation

Small-world Approximations in Spectral Segmentation (SHS), pp. 36–39.

ICPR-v2-2004-WilsonH #distance #graph

Levenshtein Distance for Graph Spectral Features (RCW, ERH), pp. 489–492.

ICPR-v3-2004-BaiYH #graph #using

Graph Matching using Spectral Embedding and Alignment (XB, HY, ERH), pp. 398–401.

ICPR-v3-2004-LuoWH #graph

Graph Manifolds from Spectral Polynomials (BL, RCW, ERH), pp. 402–405.

ICPR-v4-2004-PaclikVD #algorithm #feature model #multi

Multi-Class Extensions of the GLDB Feature Extraction Algorithm for Spectral Data (PP, SV, RPWD), pp. 629–632.

ICPR-v4-2004-ParkJZK #clustering #graph

Support Vector Clustering Combined with Spectral Graph Partitioning (JHP, XJ, HZ, RK), pp. 581–584.

KDD-2004-DhillonGK #clustering #kernel #normalisation

Kernel k-means: spectral clustering and normalized cuts (ISD, YG, BK), pp. 551–556.

DAC-2003-VasudevanR #using

Computation of noise spectral density in switched capacitor circuits using the mixed-frequency-time technique (VV, MR), pp. 538–541.

DATE-2003-LupeaPJ #analysis #named

RF-BIST: Loopback Spectral Signature Analysis (DL, UP, HJJ), pp. 10478–10483.

SIGMOD-2003-CohenM

Spectral Bloom Filters (SC, YM), pp. 241–252.

ICML-2003-Joachims #clustering #graph #learning

Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.

WCRE-2002-ShokoufandehMM #clustering

Applying Spectral Methods to Software Clustering (AS, SM, MM), pp. 3–10.

ICPR-v1-2002-CarcassoniRH #analysis #recognition

Texture Recognition through Modal Analysis of Spectral Peak Patterns (MC, ER, ERH), pp. 243–246.

ICPR-v1-2002-CarvalhoSDR #constraints #estimation #linear

Bayes Information Criterion for Tikhonov Regularization with Linear Constraints: Application to Spectral Data Estimation (PDC, AS, AD, BR), pp. 696–700.

ICPR-v1-2002-LiuS #classification #recognition #representation

A Spectral Representation for Appearance-Based Classification and Recognition (XL, AS), pp. 37–40.

ICPR-v3-2002-LuoWH02a #approach #graph #learning

Graph Spectral Approach for Learning View Structure (BL, RCW, ERH), pp. 785–788.

ICPR-v3-2002-Robles-KellyH #approach

A Graph-Spectral Approach to Surface Segmentatio (ARK, ERH), pp. 509–508.

ICPR-v4-2002-Robles-KellyH02a #approach

A Graph-Spectral Approach to Correspondence Matching (ARK, ERH), pp. 176–179.

SIGIR-2002-Shalev-ShwartzDFS #modelling #query #robust

Robust temporal and spectral modeling for query By melody (SSS, SD, NF, YS), pp. 331–338.

DATE-2001-GianiSHA #performance

Efficient spectral techniques for sequential ATPG (AG, SS, MSH, VDA), pp. 204–208.

DATE-2001-ThorntonD #diagrams #graph transformation #using

Spectral decision diagrams using graph transformations (MAT, RD), pp. 713–719.

STOC-2001-AzarFKMS #analysis

Spectral analysis of data (YA, AF, ARK, FM, JS), pp. 619–626.

KDD-2001-Dhillon #clustering #documentation #graph #using #word

Co-clustering documents and words using bipartite spectral graph partitioning (ISD), pp. 269–274.

KDD-2001-DingHZ #component #graph #web

A spectral method to separate disconnected and nearly-disconnected web graph components (CHQD, XH, HZ), pp. 275–280.

ICPR-v1-2000-RibeiroH

Texture Plane Orientation from Spectral Accumulation (ER, ERH), pp. 1802–1806.

ICPR-v1-2000-TominagaO #multi #recognition

Object Recognition by Multi-Spectral Imaging with a Liquid Crystal Filter (ST, RO), pp. 1708–1711.

ICPR-v3-2000-CamilleriP #bound #refinement

Spectral Unmixing of Mixed Pixels for Texture Boundary Refinement (KPC, MP), pp. 7096–7099.

ICPR-v3-2000-ManabeKC #metric

Simultaneous Measurement of Spectral Distribution and Shape (YM, SK, KC), pp. 3811–3814.

ICPR-v3-2000-RibeiroH00a #adaptation

A Scale Adaptive Method for Focusing Spectral Peaks (ER, ERH), pp. 3123–3126.

ICPR-v3-2000-Tajima #evaluation

Consideration and Experiments on Object Spectral Reflectance for Color Sensor Evaluation/Calibration (JT), pp. 3592–3595.

ICPR-v4-2000-KhorsheedC #multi #recognition #using #word

Multi-Font Arabic Word Recognition Using Spectral Features (MSK, WFC), pp. 4543–4546.

ICPR-1998-ManabeZI #estimation #geometry #image

Estimation of illuminant spectral distribution with geometrical information from spectral image (YM, XZ, SI), pp. 1464–1466.

ICPR-1998-NakaiMI #analysis #simulation

Simulation and analysis of spectral distributions of human skin (HN, YM, SI), pp. 1065–1067.

STOC-1997-LaffertyR

Spectral Techniques for Expander Codes (JDL, DNR), pp. 160–167.

DAC-1996-HansenS #diagrams #synthesis #using

Synthesis by Spectral Translation Using Boolean Decision Diagrams (JPH, MS), pp. 248–253.

DAC-1996-LiLLC #approach #clustering #linear

New Spectral Linear Placement and Clustering Approach (JL, JL, LTL, CKC), pp. 88–93.

ICPR-1996-Ben-ArieWR #invariant #recognition

Iconic recognition with affine-invariant spectral signatures (JBA, ZW, KRR), pp. 672–676.

ICPR-1996-LabonteDC #representation #sequence

A compact representation for stereoscopic sequences with NTSC spectral compatibility (FL, CTLD, PC), pp. 646–650.

ICPR-1996-ManabeI #image #recognition #using

Recognition of material types using spectral image (YM, SI), pp. 840–843.

ICPR-1996-SenguptaB #clustering #using

Using spectral features for modelbase partitioning (KS, KLB), pp. 65–69.

ICPR-1996-WindeattT #feature model

Analytical feature extraction and spectral summation (TW, RT), pp. 315–319.

DAC-1995-AlpertY #clustering

Spectral Partitioning: The More Eigenvectors, The Better (CJA, SZY), pp. 195–200.

KDD-1995-BuntineP #how

Intelligent Instruments: Discovering How to Turn Spectral Data into Information (WLB, TP), pp. 33–38.

STOC-1994-AlonK #graph #random

A spectral technique for coloring random 3-colorable graphs (preliminary version) (NA, NK), pp. 346–355.

DAC-1993-ChanSZ93a #clustering

Spectral K-Way Ratio-Cut Partitioning and Clustering (PKC, MDFS, JYZ), pp. 749–754.

DAC-1993-ClarkeMZFY #scalability

Spectral Transforms for Large Boolean Functions with Applications to Technology Mapping (EMC, KLM, XZ, MF, JY), pp. 54–60.