RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Multi-label dimensionality reduction via dependence maximization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Function-Function correlated multi-label protein function prediction over interaction networks
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Simultaneous image classification and annotation via biased random walk on tri-relational graph
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Annotating web images by combining label set relevance with correlation
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Image and video annotations are challenging but important tasks to understand digital multimedia contents in computer vision, which by nature is a multi-label multi-class classification problem because every image is usually associated with more than one semantic keyword. As a result, label assignments are no longer confined to class membership indications as in traditional single-label multi-class classification, which also convey important characteristic information to assess object similarity from knowledge perspective. Therefore, besides implicitly making use of label assignments to formulate label correlations as in many existing multi-label classification algorithms, we propose a novel Multi-Label Feature Transform (MLFT) approach to also explicitly use them as part of data features. Through two transformations on attributes and label assignments respectively, MLFT approach uses kernel to implicitly construct a label-augmented feature vector to integrate attributes and labels of a data set in a balanced manner, such that the data discriminability is enhanced because of taking advantage of the information from both data and label perspectives. Promising experimental results on four standard multi-label data sets from image annotation and other applications demonstrate the effectiveness of our approach.