Learning Multi-View Neighborhood Preserving Projections

By Novi Quadrianto, Christoph H. Lampert. 2011

118 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dacheng Tao
2. Yi Yang
3. Kaiye Wang
4. Ishan Misra
5. Kristen Grauman
6. Luc Van Gool
7. Liang Wang
8. Andrea Vedaldi

Citations in the corpus

• A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, methodology): “2012; Yakhnenko and Honavar 2009), metric learning (Quadrianto and Lampert 2011) and large-margin formulations (Chen et al.”
• A Survey on Multi-view Learning (2013, methodology): “Motivated by cross-media retrieval tasks, Quadrianto and Lampert (2011) studied the metric learning problem to find the joint Euclidean distance function to allow nearest neighbor queries.”
• Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020, background): “on can be accomplished contrasting representations within the same modality (Self-AVID), across modalities (Cross-AVID) or a mixture of the two (Joint-AVID). kernels, and subspace learning procedures [14,64] which seek to learn the latent space that generates all views of the data. Multi-view data i”
• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “Quadrianto and Lampert [30] propose a new metric learning scheme (Multi-View Neighborhood Preserving Projection, Multi-NPP) to project different modalities into a shared feature space, in which the Euclidean distance provides a meaningful intra-modality and inter-modality similarity.”
• An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges (2017, background): “information to achieve better metric results, which are widely used in single-media retrieval [52], [53].”
• Augmented Attribute Representations (2012, background): “The folk wisdom loss [8] then directly reflects the idea that we would like to make few mistakes in nearest neighbor classification.”
• Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval (2016, background): “Quadrianto and Lampert [18] propose a new metric learning scheme to learn projections from the data in different modalities into a shared feature space, in which the Euclidean distance provides a meaningful intramodality and inter-modality similarity.”
• Learning to Rank Using Privileged Information (2013, background): “Similarly the shared representation is also used for cross-modal retrieval [20].”
• Diversity-induced Multi-view Subspace Clustering (2015, background): “However, most of them concentrate on supervised or semi-supervised learning [1, 6, 22, 31], in which a validation set is required.”
• Learning Coupled Feature Spaces for Cross-Modal Matching (2013, background): “Quadrianto and Lampert [17] propose a metric learning approach for cross-modal matching, which considers both matching and non-matching samples.”
• Tracking via Robust Multi-task Multi-view Joint Sparse Representation (2013, background): “1Regarding the term multi-view learning [25] [29], we follow the machine learning convention, in which views refer to different feature subsets used to represent particular characteristics of an object.”
• Collective Matrix Factorization Hashing for Multimodal Data (2014, methodology): “Because they are similar in texture space while dissimilar in color space, we choose them to simulate a cross-view setting as in [16].”