Inferring decision trees using the minimum description length principle
Information and Computation
Machine Learning
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Selection of relevant genes in cancer diagnosis based on their prediction accuracy
Artificial Intelligence in Medicine
Gene selection via the BAHSIC family of algorithms
Bioinformatics
Gene extraction for cancer diagnosis by support vector machines-An improvement
Artificial Intelligence in Medicine
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Comparing ensembles of learners: detecting prostate cancer from high resolution MRI
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Orthogonal linear discriminant analysis and feature selection for micro-array data classification
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Computers in Biology and Medicine
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Inductive manifold learning using structured support vector machine
Pattern Recognition
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
The recent explosion in procurement and availability of high-dimensional gene- and protein-expression profile datasets for cancer diagnostics has necessitated the development of sophisticated machine learning tools with which to analyze them. A major limitation in the ability to accurate classify these high-dimensional datasets stems from the 'curse of dimensionality', occurring in situations where the number of genes or peptides significantly exceeds the total number of patient samples. Previous attempts at dealing with this issue have mostly centered on the use of a dimensionality reduction (DR) scheme, Principal Component Analysis (PCA), to obtain a low-dimensional projection of the high-dimensional data. However, linear PCA and other linear DR methods, which rely on Euclidean distances to estimate object similarity, do not account for the inherent underlying nonlinear structure associated with most biomedical data. The motivation behind this work is to identify the appropriate DR methods for analysis of high-dimensional gene- and protein-expression studies. Towards this end, we empirically and rigorously compare three nonlinear (Isomap, Locally Linear Embedding, Laplacian Eigenmaps) and three linear DR schemes (PCA, Linear Discriminant Analysis, Multidimensional Scaling) with the intent of determining a reduced subspace representation in which the individual object classes are more easily discriminable.