Discriminant Analysis for Fast Multiclass Data Classification Through Regularized Kernel Function Approximation

• Published in: IEEE transactions on neural networks Vol. 21; no. 6; pp. 1020 - 1029
• Main Authors: Ghorai, Santanu, Mukherjee, Anirban, Dutta, Pranab K
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2010; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Approximation; Artificial Intelligence; Automatic Data Processing; Benchmark testing; Biological and medical sciences; Cancer; Classification; Computer science; control theory; systems; Data processing. List processing. Character string processing; Discriminant Analysis; Exact sciences and technology; Function approximation; Humans; Information Storage and Retrieval; Kernel; Kernel functions; Labels; Linear discriminant analysis; Mathematical analysis; Mathematics; Medical sciences; Memory organisation. Data processing; multiclass data classification; Partitioning algorithms; Probability and statistics; Sampling methods; Sampling theory, sample surveys; Sciences and techniques of general use; Software; Statistics; support vector machine (SVM); Support vector machine classification; Support vector machines; Text categorization; Tumors; Human; Discriminant analysis; Statistical analysis; Random sampling; Bias; support vector machine (SVM); One step method; DNA chip; Very large databases; Malignant tumor; Function approximation; Linear operator; Step response; Kernel function; Cancerology; Vector space; Center of mass; Pattern classification; Vector support machine; Multiclass; multiclass data classification; Cancer
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/TNN.2010.2046646

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.