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Found 7 papers, 0 papers with code
Supervised Dictionary Learning and Sparse Representation-A Review
This review provides a broad, yet deep, view of the state-of-the-art methods for S-DLSR and allows for the advancement of research and development in this emerging area of research.
A Fast Greedy Algorithm for Generalized Column Subset Selection
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B.
Greedy Column Subset Selection for Large-scale Data Sets
The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized.
Discriminative Density-ratio Estimation
The proposed algorithm is an iterative procedure that alternates between estimating the class information for the test data and estimating new density ratio for each class.
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means.
Supervised Texture Classification Using a Novel Compression-Based Similarity Measure
To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images.
Kernelized Supervised Dictionary Learning
In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary.
