Dimension reduction in representation of the data
arXiv:0902.43891 citations
Originality Synthesis-oriented
AI Analysis
Provides a new method for dimension reduction in high-dimensional data analysis, but lacks concrete performance comparisons or numbers.
The paper presents an algorithm for finding low-dimensional sets near which most data points lie, offering an alternative to PCA for dimension reduction in high-dimensional data.
Suppose the data consist of a set $S$ of points $x_j$, $1\leq j \leq J$, distributed in a bounded domain $D\subset R^N$, where $N$ is a large number. An algorithm is given for finding the sets $L_k$ of dimension $k\ll N$, $k=1,2,...K$, in a neighborhood of which maximal amount of points $x_j\in S$ lie. The algorithm is different from PCA (principal component analysis)