Sparse subspace clustering (SSC) is a state-of-the-art method for partitioning data points into the union of subspaces. However, it is not practical for large datasets as it requires solving a LASSO problem for each data point, where the number of variables in each LASSO problem is the ...
(ArchR;https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified ...
A pattern mining-based evolutionary algorithm for large-scale sparse multiobjective optimization problems IEEE Trans. Cybern., 52 (7) (2020), pp. 6784-6797 Google Scholar [38] Deb K., Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting ap...
Several challenges have been identified in order to effectively profile user behaviour in smart mobility systems, including the learning issues for missing values, data cleansing, dimension reduction, sparse learning, and heterogeneous learning [19]. Massive amounts of raw data collected by nomadic devi...
Several challenges have been identified in order to effectively profile user behaviour in smart mobility systems, including the learning issues for missing values, data cleansing, dimension reduction, sparse learning, and heterogeneous learning [19]. Massive amounts of raw data collected by nomadic devi...
To overcome this limitation, we introduce Selective Sampling-based Scalable Sparse Subspace Clustering (S5C) algorithm which selects subsamples based on the approximated subgradients and linearly scales with the number of data points in terms of time and memory requirements. Along with the ...
Sparse subspace clustering (SSC) is a state-of-the-art method for partitioning data points into the union of subspaces. However, it is not practical for large datasets as it requires solving a LASSO problem for each data point, where the number of variables in each LASSO problem is the ...
Scalable sparse subspace clustering Image gradient feature descriptorFace clustering is an important topic in computer vision. It aims to put together facial images that belong to the same person. Spectral clustering-based algorithms are often used for accurate face......
Subspace clusteringImbalanced dataLarge-scale dataSubspace clustering methods based on expressing each data point as a linear combination of a few other data points (e.g., sparse sub-space clustering) have become a popular tool for unsupervised learning due to their empirical success and theoretical...
The method generates a consensus anchor graph across all views, representing each data point as a sparse linear combination of chosen landmarks. Unlike traditional methods that manage the anchor graph construction and the label propagation process separately, this paper proposes a unified optimization ...