Exploring pleiotropy using principal components
نویسندگان
چکیده
منابع مشابه
Persian Handwriting Analysis Using Functional Principal Components
Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
متن کاملFast Pruning Using Principal Components
We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, "Principal Components Pruning (PCP)", is based on principal component analysis of the node activations of successive layers of the network. It is simple, cheap to implement, and effective. It requires no network retraining, and does not involve calculating the...
متن کاملExploring tradeoffs in pleiotropy and redundancy using evolutionary computing
Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy...
متن کاملExploring High-dimensional Data with Robust Principal Components
For high-dimensional data of low sample size it is difficult to compute principal components in a robust way. We mention an algorithm which is highly precise and fast to compute. The robust principal components are used to compute distances of the observations in the (sub-)space of the principal components and distances to this (sub-)space. Both distance measures retain valuable information abo...
متن کاملExploring Variation: Functional Principal and Canonical Components Analysis
Now we look at how observations vary from one replication or sampled value to the next. There is, of course, also variation within observations, but we focused on that type of variation when considering data smoothing in Chapter 5. Principal components analysis, or PCA, is often the first method that we turn to after descriptive statistics and plots. We want to see what primary modes of variati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Genetics
سال: 2003
ISSN: 1471-2156
DOI: 10.1186/1471-2156-4-s1-s53