IMMC: incremental maximum margin criterion

Authors:
Jun Yan;Benyu Zhang;Shuicheng Yan;Qiang Yang;Hua Li;Zheng Chen;Wensi Xi;Weiguo Fan;Wei-Ying Ma;Qiansheng Cheng
Affiliations:
Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Peking University, Beijing, P. R. China;Hong Kong University of Science and Technology;Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Virginia Polytechnic Institute and State University, Blacksburg, VA;Virginia Polytechnic Institute and State University, Blacksburg, VA;Microsoft Research Asia, Beijing, P. R. China;Peking University, Beijing, P. R. China
Venue:
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2004

Citing 4
Cited 13

Incremental context mining for adaptive document classification

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Candid Covariance-Free Incremental Principal Component Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently handling feature redundancy in high-dimensional data

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
RCV1: A New Benchmark Collection for Text Categorization Research

The Journal of Machine Learning Research

OCFS: optimal orthogonal centroid feature selection for text categorization

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition

IEEE Transactions on Knowledge and Data Engineering
Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing

IEEE Transactions on Knowledge and Data Engineering
Mining Adaptive Ratio Rules from Distributed Data Sources

Data Mining and Knowledge Discovery
Face recognition using discriminant locality preserving projections based on maximum margin criterion

Pattern Recognition
Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

International Journal of Computer Vision
Feature Extraction Using Laplacian Maximum Margin Criterion

Neural Processing Letters
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis

SIAM Journal on Matrix Analysis and Applications
Background modeling via incremental maximum margin criterion

ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
TAKES: a fast method to select features in the kernel space

Proceedings of the 20th ACM international conference on Information and knowledge management
An incremental linear discriminant analysis using fixed point method

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A scalable supervised algorithm for dimensionality reduction on streaming data

Information Sciences: an International Journal
Incremental learning of complete linear discriminant analysis for face recognition

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.