JointL1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

Joint L1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The...

Feature extraction and classification for rectal bleeding in prostate cancer radiotherapy: A PCA based method

Combining Block-based PCA, Global PCA and LDA for Feature Extraction in Face Recognition

This paper presents a novel methodology for pattern classification, combining different feature extraction approaches, more precisely, global PCA, block-based PCA and LDA in order to improve the face classification performance. Block-based PCA is a new technique that is being widely used for feature extraction, especially in face recognition. Basically, the idea of block-based PCA is to first d...

Incremental Kernel PCA for Efficient Non-linear Feature Extraction

The Kernel Principal Component Analysis (KPCA) has been effectively applied as an unsupervised non-linear feature extractor in many machine learning applications. However, with a time complexity of O(n3), the practicality of KPCA on large datasets is minimal. In this paper, we propose an approximate incremental KPCA algorithm which allows efficient processing of large datasets. We extend a line...

Graph Laplacian for Semi-supervised Feature Selection in Regression Problems

Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past f...