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Discriminating Joint Feature Analysis for Multimedia Data Understanding
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Pattern Classification
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Convex multi-task feature learning
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Locality sensitive semi-supervised feature selection
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Discriminative learning of visual words for 3D human pose estimation
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MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
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Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
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Image Clustering Using Local Discriminant Models and Global Integration
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A unified log-based relevance feedback scheme for image retrieval
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Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection
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Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction
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A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
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Semi-supervised Feature Selection via Spectral Analysis
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Semantic manifold learning for image retrieval
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Kodak's consumer video benchmark data set
inferredcites →https://doi.org/10.1145/1631272.1631298openalex-snapshot
Ranking with local regression and global alignment for cross media retrieval
inferredcites →https://doi.org/10.1145/1823746.1823752openalex-snapshot
Semi-supervised distance metric learning for collaborative image retrieval and clustering
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Multi-label boosting for image annotation by structural grouping sparsity
inferredcites →https://doi.org/10.1145/1873951.1874129openalex-snapshot
Heterogeneous feature selection by group lasso with logistic regression
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MoSIFT: Recognizing Human Actions in Surveillance Videos
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Efficient Spectral Feature Selection with Minimum Redundancy
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
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l 2,1 -norm regularized discriminative feature selection for unsupervised learning
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Multi-Task Feature Learning
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Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Accelerated Semi-supervised Feature Selection via Adaptive Bipartite Graph
inferred← citeshttps://doi.org/10.1186/s13640-019-0428-5openalex-snapshot
A feature selection framework for video semantic recognition via integrated cross-media analysis and embedded learning
inferred← citeshttps://doi.org/10.1587/transinf.2017edp7184openalex-snapshot
READER: Robust Semi-Supervised Multi-Label Dimension Reduction
inferred← citeshttps://doi.org/10.1609/aaai.v28i1.8922openalex-snapshot
A Convex Formulation for Semi-Supervised Multi-Label Feature Selection
inferred← citeshttps://doi.org/10.1609/aaai.v33i01.33013983openalex-snapshot
Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach
inferred← citeshttps://doi.org/10.1609/aaai.v34i04.6127openalex-snapshot
To Avoid the Pitfall of Missing Labels in Feature Selection: A Generative Model Gives the Answer
inferred← citeshttps://doi.org/10.2174/2666255816666220823163913openalex-snapshot
Literature Review on Development of Feature Selection and Learning Mechanism for Fuzzy Rule-Based System
inferred← citeshttps://doi.org/10.23919/ccc50068.2020.9189331openalex-snapshot
Robust Convex Clustering with Spectral Analysis-based Feature Selection
inferred← citeshttps://doi.org/10.2478/cait-2019-0001openalex-snapshot
A Review of Feature Selection and Its Methods
inferred← citeshttps://doi.org/10.25148/etd.fidc007772openalex-snapshot
Multimodal Data Analytics and Fusion for Data Science
inferred← citeshttps://doi.org/10.3390/fi13060158openalex-snapshot
Ontology-Based Feature Selection: A Survey
inferred← citeshttps://doi.org/10.3390/sym16121646openalex-snapshot
Weighted Multiview K-Means Clustering with L2 Regularization
inferred← citeshttps://doi.org/10.36227/techrxiv.171591394.42973650/v1openalex-snapshot
Semi-supervised feature selection using maximum mutual information and minimum correlated feature set retrieved by augmented learning
inferred← citeshttps://doi.org/10.3923/itj.2014.102.109openalex-snapshot
Image Annotation Based on Joint Feature Selection with Sparsity
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