A low rank structural large margin method for cross-modal ranking
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Summary
A general cross-modal ranking algorithm to optimize the listwise ranking loss with a low rank embedding, which is called Latent Semantic Cross-Modal Ranking (LSCMR) and shows significant improvements over the state-of-the-art methods.
- Type
- article
- Published
- 2013-07-28
- Cited by
- 52
- References
- 32
- OpenAlex
- https://openalex.org/W2038436420
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15630668
Keywords
Ranking (information retrieval), Margin (machine learning), Computer science, Learning to rank, Modal
References
- Topic regression multi-modal Latent Dirichlet Allocation for image annotation
- Learning to Rank for Information Retrieval and Natural Language Processing
- Learning Multimodal Dictionaries
- Modeling annotated data
- Relations Between Two Sets of Variates
- Learning to rank with (a lot of) word features
- Cutting-plane training of structural SVMs
- Optimizing search engines using clickthrough data
- A support vector method for multivariate performance measures
- Generalized Multiview Analysis: A discriminative latent space
- Smoothing clickthrough data for web search ranking
- Canonical Correlation Analysis: An Overview with Application to Learning Methods
- Large Margin Methods for Structured and Interdependent Output Variables
- A new approach to cross-modal multimedia retrieval
- Learning to rank: from pairwise approach to listwise approach
- Query by document
- Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
- Learning cross-modality similarity for multinomial data
- A support vector method for optimizing average precision
- Large-scale linear support vector regression
Cited by
- Pattern Recognition
- Effective deep learning-based multi-modal retrieval
- Compositional Correlation Quantization for Large-Scale Multimodal Search
- Multi-modal Mutual Topic Reinforce Modeling for Cross-media Retrieval
- Discriminative coupled dictionary hashing for fast cross-media retrieval
- Image-Text Cross-Modal Retrieval via Modality-Specific Feature Learning
- Sparse Multi-Modal Hashing
- Deep Compositional Cross-modal Learning to Rank via Local-Global Alignment
- Structural Bregman Distance Functions Learning to Rank with Self-Reinforcement
- The classification of multi-modal data with hidden conditional random field
- Learning Multimodal Neural Network with Ranking Examples
- Cross-Modal Learning to Rank via Latent Joint Representation
- Cross-media semantic representation via bi-directional learning to rank
- Rank Learning Model of Cross-media Retrieval based on Structured SVM
- Semantic Boosting Cross-Modal Hashing for efficient multimedia retrieval
- Cross-Modal Self-Taught Hashing for large-scale image retrieval
- Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval
- Learning of Multimodal Representations With Random Walks on the Click Graph
- Effective Multi-Modal Retrieval based on Stacked Auto-Encoders
- Learning unified sparse representations for multi-modal data
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