Cotransfer Learning Using Coupled Markov Chains with Restart

This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.

Authors

• Qingyao Wu South China University of Technology Peng Cheng Laboratory
• Michael K. Ng
• Yunming Ye Harbin Institute of Technology, Shenzhen

Keywords

• Machine learning
• Markov processes
• Training data
• Ranking
• Classification algorithms
• Learning systems
• Iterative methods
• Learning Algorithms
• Classification Task
• Feature Space
• Transition Probabilities
• Transfer Learning
• Classification Datasets
• Domain Data
• Conventional Machine Learning
• Machine Learning Strategies
• Data Mining Applications
• Computation Time
• Support Vector Machine
• K-nearest Neighbor
• Average Accuracy
• Random Walk
• Class Labels
• Text Data
• Learning Problem
• Support Vector Machine Algorithm
• Test Instances
• Scale-invariant Feature Transform
• Transition Probability Matrix
• Unlabeled Instances
• Steady-state Probability
• Percentage Of Instances
• Average Accuracy Rate
• Gaussian Kernel Function
• cotransfer learning
• coupled Markov chains
• classification
• labels ranking
• intelligent systems