A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

Medical Image Analysis

Volumn 38, Issue , 2017, Pages 205-214

  Zhu, Xiaofeng ^a   Suk, Heung Il ^b   Wang, Li ^a   Lee, Seong Whan ^b   Shen, Dinggang ^a,b  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b Korea University   (South Korea)

Author keywords

Alzheimer's disease; Feature selection; Manifold learning; MCI conversion; Sparse coding

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; NEURODEGENERATIVE DISEASES; NEUROIMAGING; REGRESSION ANALYSIS;

ALZHEIMER'S DISEASE; COMPUTATIONALLY EFFICIENT; FEATURE SELECTION METHODS; MANIFOLD LEARNING; SPARSE CODING; STATE-OF-THE-ART METHODS; SUPPORT VECTOR CLASSIFICATION; SUPPORT VECTOR REGRESSION MODELS;

FEATURE EXTRACTION;

AGED; ALZHEIMER DISEASE; ALZHEIMER DISEASE ASSESSMENT SCALE; ALZHEIMER DISEASE ASSESSMENT SCALE COGNITIVE SUBSCALE; ARTICLE; AUTOMATED PATTERN RECOGNITION; CLINICAL EFFECTIVENESS; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEATURE SELECTION METHOD; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MINI MENTAL STATE EXAMINATION; NEUROIMAGING; PREDICTION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; ALGORITHM; DIAGNOSTIC IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES;

AGED; ALGORITHMS; ALZHEIMER DISEASE; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; PATTERN RECOGNITION, AUTOMATED; SUPPORT VECTOR MACHINE;

EID: 84949058046     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2015.10.008     Document Type: Article

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