Results 81 to 90 of about 14,028 (258)
Identifying subspace gene clusters from microarray data using low-rank representation.
PLoS ONE, 2013 Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR
Yan Cui, Chun-Hou Zheng, Jian Yang
doaj +1 more source
Advanced Intelligent Systems, EarlyView.HTFC gets 3D refractive index tomograms of flowing cells. Label‐free monocytes are engineered to express patterns of cytoplasmic vacuoles. From the tomogram, an efficient dimensionality reduction is operated. Interpretable features are extracted to classify the expression severity of phenotypes coexisting in each cell, visually represented by a seven ...
Marika Valentino +9 more
wiley +1 more source
A General Approach for Achieving Supervised Subspace Learning in Sparse Representation
IEEE Access, 2019 Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature.
Jianshun Sang +2 more
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SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement. [PDF]
Genomics Proteomics Bioinformatics, 2021 Liang Z +7 more
europepmc +1 more source
Asian Journal of Control, EarlyView.Abstract Large swarms often adopt a hierarchical network structure that incorporates information aggregation. Although this approach offers significant advantages in terms of communication efficiency and computational complexity, it can also lead to degradation due to information constraints.
Kento Fujita, Daisuke Tsubakino
wiley +1 more source
Multiscale Grassmann manifolds for single-cell RNA-seq data analysis
Machine Learning: Science and TechnologySingle-cell RNA-seq data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector in Euclidean space, which limits their ability to capture intrinsic
Xiang Xiang Wang +2 more
doaj +1 more source
On structural controllability in complex networks with periodic switching topologies
Asian Journal of Control, EarlyView.Abstract This paper investigates the structural controllability of complex networks with periodic switching topologies. First, several graph transformations that preserve structural controllability are demonstrated. Based on the n‐walk theory, a criterion is derived that determines structural controllability by analyzing only the joint graph within a ...
Jingrui Hou +3 more
wiley +1 more source
Learning Visual-Semantic Subspace Representations
The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)
Gabriel Moreira +3 more
openaire +3 more sources
Machine Learning Paradigm for Advanced Battery Electrolyte Development
Carbon Energy, EarlyView.Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su +4 more
wiley +1 more source
Hypergraph Representation via Axis-Aligned Point-Subspace Cover [PDF]
Discrete Mathematics & Theoretical Computer ScienceWe propose a new representation of $k$-partite, $k$-uniform hypergraphs, that is, a hypergraph with a partition of vertices into $k$ parts such that each hyperedge contains exactly one vertex of each type; we call them $k$-hypergraphs for short.
Oksana Firman, Joachim Spoerhase
doaj +1 more source