Discriminative topic modeling based on manifold learning

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 653-661

  Huh, Seungil ^a   Fienberg, Stephen E ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Dimensionality reduction; Document classification; Semi supervised learning; Topic modeling

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; DATA ANALYSIS; DIMENSIONALITY REDUCTION; DISCRIMINATING POWER; DOCUMENT CLASSIFICATION; EMPIRICAL EVIDENCE; HIDDEN STRUCTURES; LAPLACIANS; LATENT DIRICHLET ALLOCATIONS; LOCAL CONSISTENCY; LOCAL MANIFOLD STRUCTURE; MANIFOLD LEARNING; MODEL FITTING; NONLINEAR DIMENSIONALITY REDUCTION; PARETO IMPROVEMENTS; PROBABILISTIC LATENT SEMANTIC ANALYSIS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; TEXT CORPORA; TEXT DOCUMENT; TOPIC MODEL; TOPIC MODELING;

DATA REDUCTION; INFORMATION RETRIEVAL SYSTEMS; SUPERVISED LEARNING; TEXT PROCESSING;

MODELS;

EID: 77956221584     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835888     Document Type: Conference Paper

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