计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 62-67.doi: 10.11896/j.issn.1002-137X.2019.02.010
张善文, 文国秋, 张乐园, 李佳烨
ZHANG Shan-wen, WEN Guo-qiu, ZHANG Le-yuan, LI Jia-ye
摘要： 鉴于传统属性选择算法无法捕捉属性之间的关系的问题,文中提出了一种非线性属性选择方法。该方法通过引入核函数,将原始数据集投影到高维的核空间,因在核空间内进行运算,进而可以考虑到数据属性之间的关系。由于核函数自身的优越性,即使数据通过高斯核投影到无穷维的空间中,计算复杂度亦可以控制得较小。在正则化因子的限制上,使用两种范数进行双重约束,不仅提高了算法的准确率,而且使得算法实验结果的方差仅为0.74,远小于其他同类对比算法,且算法更加稳定。在8个常用的数据集上将所提算法与6个同类算法进行比较,并用SVM分类器来测试分类准确率,最终该算法得到最少1.84%,最高3.27%,平均2.75%的提升。
中图分类号:
| [1]ZHU X,SUK H I,SHEN D.Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer’s Disease Diagnosis[C]∥Computer Vision and Pattern Recognition.IEEE,2014:3089-3096. [2]GU Q,LI Z,HAN J.Joint feature selection and subspace lear- ning[C]∥International Joint Conference on Artificial Intelligence.AAAI Press,2011:1294-1299. [3]ZHU X,HUANG Z,CHENG H,et al.Sparse hashing for fast multimedia search[J].Acm Transactions on Information Systems,2013,31(2):1-24. [4]ZHU X,HUANG Z,YANG Y,et al.Self-taught dimensionality reduction on the high-dimensional small-sized data[J].Pattern Recognition,2013,46(1):215-229. [5]PYATYKH S,HESSER J,ZHENG L.Image noise level estima- tion by principal component analysis[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(2):687-699. [6]KONIETSCHKE F,PAULY M.Bootstrapping and permuting paired t-test type statistics[J].Statistics & Computing,2014,24(3):283-296. [7]LIIMATAINEN K,HEIKKILÄ R,YLIHARJA O,et al.Sparse logistic regression and polynomial modelling for detection of artificial drainage networks[J].Remote Sensing Letters,2015,6(4):311-320. [8]BENABDESLEM K,HINDAWI M.Constrained laplacian score for semi-supervised feature selection[C]∥Machine Learning and Knowledge Discovery in Databases－European Conference Proceedings.DBLP,2011:204-218. [9]ZHANG S,CHENG D,ZONG M,et al.Self-representation nearest neighbor search for classification[J].Neurocomputing,2016,195(C):137-142. [10]DENG Z,ZHANG S,YANG L,et al.Sparse sample self-representation for subspace clustering[J].Neural Computing & Applications,2018,29(11):43-49. [11]VARMA M,BABU B R.More generality in efficient multiple kernel learning[C]∥International Conference on Machine Learning.ACM,2009:1065-1072. [12]COMANICIU D,RAMESH V,MEER P P.Kernel-Based Object Tracking[J].Pattern Analysis & Machine Intelligence,2003,25(5):564-575. [13]GONG Y H,ZONG M,ZHU Y H,et al.Knn regression based on mixed-norm reconstruction [J].Computer Applications and Software,2016(2):232-236.(in Chinese) 龚永红,宗鸣,朱永华,等.基于混合模重构的kNN回归[J].计算机应用与软件,2016(2):232-236. [14]WANG H,NIE F,HUANG H,et al.Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance[C]∥International Conference on Compu-ter Vision.2011:557. [15]GU Q,LI Z,HAN J.Linear discriminant dimensionality reduction[C]∥Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Springer Berlin Heidelberg,2011:549-564. [16]ZHU X,ZHANG L,HUANG Z.A sparse embedding and least variance encoding approach to hashing[J].IEEE Transactions on Image Processing,2014,23(9):3737-3750. [17]ZHU X,SUK H I,SHEN D.A Novel Multi-relation Regularization Method for Regression and Classification in AD Diagnosis[C]∥International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer International Publishing,2014:401-408. [18]UCI repository of machine learning datasets [EB/OL]. [2016-05-27].http://archive.icsuci.edu/ml. [19]NIE F,HUANG H,CAI X,et al.Efficient and robust feature selection via joint 2,1 -norms minimization[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2010:1813-1821. [20]CHANG X,NIE F,YANG Y,et al.A convex formulation for semi-supervised multi-label feature selection[C]∥Twenty-Eighth AAAI Conference on Artificial Intelligence.AAAI Press,2014:1171-1177. [21]CAI D,ZHANG C,HE X.Unsupervised feature selection for multi-cluster data[C]∥ACM SIGKDD International Confe-rence on Knowledge Discovery and Data Mining.ACM,2010:333-342. [22]YAMADA M,JITKRITTUM W,SIGAL L,et al.High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso[J].Neural Computation,2012,26(1):185-207. [23]NIE F,ZHU W,LI X.Unsupervised feature selection with structured graph optimization[C]∥Thirtieth AAAI Conference on Artificial Intelligence.AAAI Press,2016:1302-1308. [24]YANG Y,SHEN H T,MA Z,et al.l 2,1 -norm regularized discriminative feature selection for unsupervised learning[C]∥International Joint Conference on Artificial Intelligence.AAAI Press,2011:1589-1594. [25]LIBSVM-ALibrary for Support Vector Machinces [EB/OL]. [2015-04-10].http://www/csie.ntu.edu.tw/~cjlin/libsvm. [26]ZHAO Z,HE X,CAI D,et al.Graph Regularized Feature Selection with Data Reconstruction[J].IEEE Transactions on Knowledge & Data Engineering,2016,28(3):689-700. [27]XUE H,SONG Y,XU H M.Multiple Indefinite Kernel Lear- ning for Feature Selection[C]∥Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:3210-3216. |
| [1] | 李霞, 马茜, 白梅, 王习特, 李冠宇, 宁博. RIIM:基于独立模型的在线缺失值填补 RIIM:Real-Time Imputation Based on Individual Models 计算机科学, 2022, 49(8): 56-63. https://doi.org/10.11896/jsjkx.210600180 |
| [2] | 孙晓寒, 张莉. 基于评分区域子空间的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace 计算机科学, 2022, 49(7): 50-56. https://doi.org/10.11896/jsjkx.210600062 |
| [3] | 刘云, 董守杰. 基于CUDA核函数的多路视频图像拼接加速算法 Acceleration Algorithm of Multi-channel Video Image Stitching Based on CUDA Kernel Function 计算机科学, 2022, 49(6A): 441-446. https://doi.org/10.11896/jsjkx.210600043 |
| [4] | 汪晋, 刘江. 基于GPU的并行DILU预处理技术 GPU-based Parallel DILU Preconditioning Technique 计算机科学, 2022, 49(6): 108-118. https://doi.org/10.11896/jsjkx.210300259 |
| [5] | 王美玲, 刘晓楠, 尹美娟, 乔猛, 荆丽娜. 基于评论和物品描述的深度学习推荐算法 Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions 计算机科学, 2022, 49(3): 99-104. https://doi.org/10.11896/jsjkx.210200170 |
| [6] | 孙圣姿, 郭炳晖, 杨小博. 用于多模态语义分析的嵌入共识自动编码器 Embedding Consensus Autoencoder for Cross-modal Semantic Analysis 计算机科学, 2021, 48(7): 93-98. https://doi.org/10.11896/jsjkx.200600003 |
| [7] | 孙明玮, 司维超, 董琪. 基于多维度数据的网络服务质量的综合评估研究 Research on Comprehensive Evaluation of Network Quality of Service Based on Multidimensional Data 计算机科学, 2021, 48(6A): 246-249. https://doi.org/10.11896/jsjkx.200900131 |
| [8] | 马凤飞, 蔺素珍, 刘峰, 王丽芳, 李大威. 基于语义对比生成对抗网络的高倍欠采MRI重建 Semantic-contrast Generative Adversarial Network Based Highly Undersampled MRI Reconstruction 计算机科学, 2021, 48(4): 169-173. https://doi.org/10.11896/jsjkx.200600047 |
| [9] | 鲍志强, 陈卫东. 基于最大后验估计的谣言源定位器 Rumor Source Detection in Social Networks via Maximum-a-Posteriori Estimation 计算机科学, 2021, 48(4): 243-248. https://doi.org/10.11896/jsjkx.200400053 |
| [10] | 李培冠, 於志勇, 黄昉菀. 基于稀疏表示的电力负荷数据补全 Power Load Data Completion Based on Sparse Representation 计算机科学, 2021, 48(2): 128-133. https://doi.org/10.11896/jsjkx.191200152 |
| [11] | 胡蓉, 阳王东, 王昊天, 罗辉章, 李肯立. 基于GPU加速的并行WMD算法 Parallel WMD Algorithm Based on GPU Acceleration 计算机科学, 2021, 48(12): 24-28. https://doi.org/10.11896/jsjkx.210600213 |
| [12] | 徐兵, 弋沛玉, 王金策, 彭舰. 知识图谱嵌入的高阶协同过滤推荐系统 High-order Collaborative Filtering Recommendation System Based on Knowledge Graph Embedding 计算机科学, 2021, 48(11A): 244-250. https://doi.org/10.11896/jsjkx.210100211 |
| [13] | 邵政毅, 陈秀宏. 基于样本特征核矩阵的稀疏双线性回归 Sample Feature Kernel Matrix-based Sparse Bilinear Regression 计算机科学, 2021, 48(10): 185-190. https://doi.org/10.11896/jsjkx.200800219 |
| [14] | 田旭, 常侃, 黄升, 覃团发. 基于残差字典及协作表达的单图像超分辨率算法 Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation 计算机科学, 2020, 47(9): 135-141. https://doi.org/10.11896/jsjkx.190600146 |
| [15] | 程中建, 周双娥, 李康. 基于多尺度自适应权重的稀疏表示目标跟踪算法 Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight 计算机科学, 2020, 47(6A): 181-186. https://doi.org/10.11896/JsJkx.190500093 |
|
||
首页