Application-oriented purely semantic precision and recall for ontology mapping evaluation
Knowledge-Based Systems
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Graph Regularized Sparse Coding for Image Representation
IEEE Transactions on Image Processing
Discriminative multi-manifold analysis for face recognition from a single training sample per person
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised learning using random walk limiting probabilities
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach.