Some ideas from Holbrey's report

I'm posting some interesting notes from Richard Holbrey's report on dimensionality reduction:

• Imola K. Fodor, June 2002 - "A survey of dimension reduction techniques"
• Hierarchical Clustering Explorer for Interactive Exploration of Multidimensional Data
• Key data analysis functions available in the GeneSpring Platform software (Docs): kNN, Cros gene error model, Multiple testing corrections, One-way ANOVA, Two-way ANOVA, PCA, QT clustering
  
• Clustering analysis
• Clustering software
• J.J. Verbeek (PhD thesis) 2004 - "Mixture Models for Clustering and Dimension Reduction"
• Michael I. Jordan - "The Kernel trick" (Transforming the data can make it linearly separable)
  
• Cross validation

Multi-dimensional data visualisation

Considering my basic system to date is based on just 9 features, this still means the system is processing data of dimension 48. To be able to visualise this data would be of benefit for several reasons, including my own understanding of the feature data being extracted and for explanatory purposes to the eventual readers of my thesis. I came across an excellent report by Richard Holbrey of Leeds University who describes data dimension reduction in simplicity and offers good insight into available tools for data visualisation. It's well worth the read.

It's been awhile...

It certainly been awhile since I last posted. I'm currently rebuilding my AMIIS. The AMIIS will be self contained, extensible and 'hopefully' quite impressive. I've the data processing working so far. I got kNNs to work on the data yesterday and trying to get GMMs working today. I'm interfacing with other toolboxes so hence the delay. I do, however, envision a restructuring of the system again to be more inline with the toolboxes I'm using. It all depends on how successful each toolbox fairs at classifying...???