Journal Article10.1016/J.PATCOG.2017.04.030
Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search
Yupei Zhang,Ming Xiang,Bo Yang +2 more
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TL;DR: This work introduces the graph Laplacian regularization to preserve the local structure of the original data into reduced space, which is indeed beneficial to ANN, and imposes non-negativity constraints such that the retrieval code can more effectively reflect the neighborhood relation, thereby cutting down on unnecessary hash collision.
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About: This article is published in Pattern Recognition. The article was published on 01 Oct 2017. The article focuses on the topics: K-SVD & Nearest neighbor search.
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Citations
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When is nearest neighbor meaningful
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Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning
Yupei Zhang,Xiuxiu He,Zhen Tian,Jiwoong Jason Jeong,Yang Lei,Tonghe Wang,Qiulan Zeng,Ashesh B. Jani,Walter J. Curran,Pretesh Patel,Tian Liu,Xiaofeng Yang +11 more
TL;DR: This technique provides accurate multi-needle detection for US-guided HDR prostate brachytherapy, facilitating the clinical workflow and can correctly detect 95% of needles with a tip location error of 1.01 mm on the prostate dataset.
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Hierarchical Clustering-Based Graphs for Large Scale Approximate Nearest Neighbor Search
TL;DR: A novel search technique to guide the navigation on the graph without computing exhaustively the distances to all neighbors in each step of the search, just to those in the direction of the query.
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Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis
TL;DR: A novel learning-based model for student knowledge diagnosis, dubbed Meta-knowledge Dictionary Learning ( metaDL), which aims to learn a meta-knowledge dictionary from student responses, and interprets expert concepts with the resulting meta-knowledges.
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Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction
TL;DR: Graph regularized robust matrix factorization (GRMF), a new MF method based on the recent robust MF version, which is robust to various data problem and achieves more effective features in comparison with other methods.
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References
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TL;DR: This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.
•Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
John C. Duchi,Elad Hazan,Yoram Singer +2 more
- 01 Jan 2010
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.
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