InteractiveJavaTools for Exploring High-Dimensional Data
نویسندگان
چکیده
منابع مشابه
Methods for regression analysis in high-dimensional data
By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...
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 کاملA Framework for Exploring High-Dimensional Geometry
To extract useful information from high-dimensional geometric or structural data, we must find low-dimensional projections that are informative and interesting to look at. The conventional, manual-interaction methods used for this purpose are ineffective when the dimensionality of the data is high, or when the geometric models are complex. Standard methods for determining useful low-dimensional...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کاملExploring high-dimensional classification boundaries
Given p-dimensional training data containing d groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Typically, the classifier is treated as a black box and the focus is on finding classifiers with good predictive accuracy. For many problems the ability to predict new observations accurately is sufficient, but it is very interesting to lear...
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: Journal of Statistical Software
سال: 2001
ISSN: 1548-7660
DOI: 10.18637/jss.v006.i01