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JMLR
2010
144views more  JMLR 2010»
11 years 4 months ago
Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
Alexander Ilin, Tapani Raiko
WEBI
2001
Springer
12 years 2 months ago
Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering
: Automated collaborative filtering is a popular technique for reducing information overload. In this paper, we propose a new approach for the collaborative filtering using local...
Katsuhiro Honda, Nobukazu Sugiura, Hidetomo Ichiha...
BMCBI
2007
149views more  BMCBI 2007»
11 years 9 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park
BMCBI
2006
116views more  BMCBI 2006»
11 years 9 months ago
Integrative missing value estimation for microarray data
Background: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performan...
Jianjun Hu, Haifeng Li, Michael S. Waterman, Xiang...
BMCBI
2006
211views more  BMCBI 2006»
11 years 9 months ago
Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding s
Background: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene ex...
Xian Wang, Ao Li, Zhaohui Jiang, Huanqing Feng
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