Sparse multiple kernel learning for signal processing applications

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 32, Issue 5, 2010, Pages 788-798

  Subrahmanya, Niranjan ^a   Shin, Yung C ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Composite kernel learning; Feature group selection; Heterogeneous data fusion; Sensor selection

Indexed keywords

CANCER DETECTION; COMPOSITE KERNELS; DUAL PROBLEM; EFFICIENT IMPLEMENTATION; FEATURE GROUPS; FEATURE SELECTION; FUSION SENSOR; HETEROGENEOUS DATA; HYPERSPECTRAL IMAGERY; INTERPRETABILITY; ITERATIVE LEARNING; KERNEL BASED METHODS; KERNEL SELECTION; KERNEL TRICK; KERNEL WEIGHT; LAND COVER CLASSIFICATION; LINEAR COMBINATIONS; LINEAR MODEL; MASS SPECTRA; MODEL DEVELOPMENT; MODEL PARAMETER ESTIMATION; MULTIPLE KERNEL LEARNING; NATURAL EXTENSION; NIR SPECTRUM; NON-LINEAR MODEL; PENALTY TERM; POSSIBLE SOLUTIONS; PRIMAL PROBLEM; SIGNAL PROCESSING APPLICATIONS; SINGLE KERNEL; SPARSE MULTIPLES;

INFORMATION FUSION; MASS SPECTROMETRY; PARAMETER ESTIMATION; PROBLEM SOLVING; REMOTE SENSING; SENSOR DATA FUSION; SIGNAL PROCESSING;

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; METHODOLOGY; SIGNAL PROCESSING; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77949784467     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2009.98     Document Type: Article

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