A scalable algorithm for learning a mahalanobis distance metric

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5996 LNCS, Issue PART 3, 2010, Pages 299-310

  Kim, Junae ^a,b   Shen, Chunhua ^a,b   Wang, Lei ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATA; CLASSIFICATION ACCURACY; COMPUTATIONAL LOADS; COMPUTATIONAL OVERHEADS; CONVEX COMBINATIONS; CONVEX OPTIMIZATION PROBLEMS; DISTANCE METRICS; EFFECTIVE SOLUTION; EXPERIMENTAL STUDIES; FEATURE VECTORS; GENERALIZATION PERFORMANCE; GRADIENT DESCENT METHOD; INTRINSIC CHARACTERISTICS; LOSS FUNCTIONS; MAHALANOBIS DISTANCES; MATRIX; METRIC LEARNING; POSITIVE SEMIDEFINITE MATRICES; SCALABLE ALGORITHMS; TRAINING SAMPLE; VISUAL RECOGNITION;

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; CONVEX OPTIMIZATION; MATRIX ALGEBRA; OPTIMIZATION;

LEARNING ALGORITHMS;

EID: 78650423534     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12297-2_29     Document Type: Conference Paper

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