Semi-supervised Sparse Feature Selection based on Multi-view Hessian Regularization

2015 ◽  
Author(s):  
Caijuan Shi ◽  
Xiaodong Yan ◽  
Jian Liu ◽  
Liping Liu
Keyword(s):  
Feature Selection ◽  
Sparse Feature Selection

Group sparse feature selection on local learning based clustering

2016 ◽  
Vol 171 ◽  
pp. 1118-1130 ◽  
Author(s):  
Yue Wu ◽  
Can Wang ◽  
Jiajun Bu ◽  
Chun Chen
Keyword(s):  
Feature Selection ◽  
Local Learning ◽  
Sparse Feature Selection

Nonlinear sparse feature selection algorithm via low matrix rank constraint

2018 ◽  
Vol 78 (23) ◽  
pp. 33319-33337
Author(s):  
Leyuan Zhang ◽  
Yangding Li ◽  
Jilian Zhang ◽  
Pengqing Li ◽  
Jiaye Li
Keyword(s):  
Feature Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Matrix Rank ◽  
Rank Constraint ◽  
Sparse Feature Selection

scholarly journals Machine learning methods in the computational biology of cancer

2014 ◽  
Vol 470 (2167) ◽  
pp. 20140081 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Feature Selection ◽  
Classification Problems ◽  
Open Problems ◽  
Machine Learning Methods ◽  
Selection For ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Sparse Feature Selection

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

scholarly journals Sparse feature selection for classification and prediction of metastasis in endometrial cancer

BMC Genomics ◽  
2017 ◽  
Vol 18 (S3) ◽  
Author(s):  
Mehmet Eren Ahsen ◽  
Todd P. Boren ◽  
Nitin K. Singh ◽  
Burook Misganaw ◽  
David G. Mutch ◽  
...  
Keyword(s):  
Feature Selection ◽  
Endometrial Cancer ◽  
Prediction Of Metastasis ◽  
Selection For ◽  
Sparse Feature Selection

scholarly journals Incremental learning-based visual tracking with weighted discriminative dictionaries

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989015
Author(s):  
Penggen Zheng ◽  
Huimin Zhao ◽  
Jin Zhan ◽  
Yijun Yan ◽  
Jinchang Ren ◽  
...  
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Texture Features ◽  
Reconstruction Error ◽  
Selection Strategy ◽  
Position Information ◽  
Color Features ◽  
Training Samples ◽  
Noise Energy ◽  
Sparse Feature Selection

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.

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