The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns
Information Processing and Management: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
Semi-Supervised Learning
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mining Combinatorial Effects on Quantitative Traits from Protein Expression Data
Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Semi-supervised Bayesian ARTMAP
Applied Intelligence
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
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Microarray technology has generated vast amounts of gene expression data with distinct patterns. Based on the premise that genes of correlated functions tend to exhibit similar expression patterns, various machine learning methods have been applied to capture these specific patterns in microarray data. However, the discrepancy between the rich expression profiles and the limited knowledge of gene functions has been a major hurdle to the understanding of cellular networks. To bridge this gap so as to properly comprehend and interpret expression data, we introduce Relevance Feedback to microarray analysis and propose an interactive learning framework to incorporate the expert knowledge into the decision module. In order to find a good learning method and solve two intrinsic problems in microarray data, high dimensionality and small sample size, we also propose a semisupervised learning algorithm: Kernel Discriminant-EM (KDEM). This algorithm efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends the linear algorithm in Discriminant-EM (DEM) to a kernel algorithm to handle nonlinearly separable data in a lower dimensional space. The Relevance Feedback technique and KDEM together construct an efficient and effective interactive semisupervised learning framework for microarray analysis. Extensive experiments on the yeast cell cycle regulation data set and Plasmodium falciparum red blood cell cycle data set show the promise of this approach.