A shared-subspace learning framework for multi-label classification

2010 ◽  
Vol 4 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Shuiwang Ji ◽  
Lei Tang ◽  
Shipeng Yu ◽  
Jieping Ye
Keyword(s):  
Subspace Learning ◽  
Learning Framework

A Pareto-Based Sparse Subspace Learning Framework

2019 ◽  
Vol 49 (11) ◽  
pp. 3859-3872
Author(s):  
Juanjuan Luo ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Shuyuan Yang ◽  
Wenping Ma
Keyword(s):  
Subspace Learning ◽  
Learning Framework

On‐line print‐defect detecting in an incremental subspace learning framework

Sensor Review ◽  
2011 ◽  
Vol 31 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Xiaogang Sun ◽  
Liang Zhang ◽  
Bin Chen
Keyword(s):  
Subspace Learning ◽  
Learning Framework ◽  
On Line

scholarly journals Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

2019 ◽  
Author(s):  
Jianing Xi ◽  
Xiguo Yuan ◽  
Minghui Wang ◽  
Ao Li ◽  
Xuelong Li ◽  
...  
Keyword(s):  
Ground Truth ◽  
Subspace Learning ◽  
Detection Methods ◽  
Supplementary Information ◽  
Driver Gene ◽  
Key Factors ◽  
Gene Detection ◽  
Driver Genes ◽  
Learning Framework ◽  
Mutation Data

AbstractMotivationDetecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients.ResultsBy incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes.Availability and implementationThe source code is publicly available at https://github.com/JianingXi/DriverSub.Supplementary informationSupplementary data are available at Bioinformatics online.

Short-Term Auditory Memory, Oral Perception, and Experimental Sound Learning

1969 ◽  
Vol 12 (1) ◽  
pp. 185-192 ◽  
Author(s):  
John L. Locke
Keyword(s):  
Memory Span ◽  
Auditory Memory ◽  
Span Task ◽  
Short Term ◽  
Learning Framework ◽  
Sound Learning ◽  
Oral Perception

Ten children with high scores on an auditory memory span task were significantly better at imitating three non-English phones than 10 children with low auditory memory span scores. An additional 10 children with high scores on an oral stereognosis task were significantly better at imitating two of the three phones than 10 children with low oral stereognosis scores. Auditory memory span and oral stereognosis appear to be important subskills in the learning of new articulations, perhaps explaining their appearance in the literature as “etiologies” of disordered articulation. Although articulation development and the experimental acquisition of non-English phones have certain obvious differences, they seem to share some common processes, suggesting that the sound learning framework may be an efficacious technique for revealing otherwise inaccessible information.

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