A Brief Introduction to Relational Perspective
Map
Relational perspective map (RPM), developed by James X. Li[1], is a general purpose
method for visualizing distance information of data points in high dimensional
spaces.
The starting point of the RPM algorithm is a set of data point
s_{i,} i=1,...,N, and a distance matrix δ_{ij}.
The matrix δ_{ij}, called the relational distance, is
the numeric representation of a relationship between the data points. The
goal of the RPM algorithm is to map the data points s_{i}
into a two or three dimensional map so that Euclidean distances d_{ij}
between the image points visually approaches δ_{ij }as
much as possible. The resulting lower dimensional map is called Relational Perspective Map, the matrix d_{ij} is called the Image Distance Matrix. From geometric point of view, an RPM map attempts to preserve as much
as possible the distance information within the original dataset.
The following figure demonstrates how the RPM algorithm works to create 2D maps:
it first maps data points to the surface of a torus, then unfolds the torus
surface by a vertical and a horizontal cut. This second step is relatively straightforward (see here for a short video showing the transformation between
rectangle and torus), the innovation of the RPM algorithm lies in mapping the data
points to the torus surface so that the distances between the image points
resembles the distances between data points.
In order the find the best mapping, the RPM algorithm defines an energy
function as follows:
where p is an algorithm parameter called the rigidity, d_{ij
} is the geodesic block distance between two image
points on the torus surface. The RPM algorithm then uses gradient descent
optimization method to find the lowestenergy configuration. The rigidity
parameter, which is normally a value between 1 and +1, alters the energy
landscape in a global manner, so that the resulting RPM maps have different
characteristics.
To better understand the RPM algorithm it is helpful to consider the
image points on the torus as a forcedirected, multiparticale system with
mutual repulsive forces between them; and consider the energy E_{p}
as a kind of total potential energy. According to physics, the repulsive
force is characterized as proportional to relational distance:
The optimization strategy is to minimize the energy E_{p} by simulating a dynamic system directed by the force given by above form. Since
points with larger relational distances between them correspond to larger
repulsive force on the torus, their image points on the torus should be
further apart from each other.
The key idea of RPM algorithm is its exploitation of the properties of
closed manifold (the torus) to keep the configuration in balance.
(Competing approaches are identified in the next section.) Whereas
other nonlinear methods apply, directly or indirectly, attractive
force to map closely related points to closely located positions,
the RPM algorithm maps closely related points within a closely
located area by modeling the collective repulsive force of all points.
This approach makes RPM a true, and (as far as we know) the
only, global mapping algorithm.
It should be noted here that the RPM
algorithm relaxes one condition of the original problem setting: the
resulting map is not a normal rectangle, but a torus. This means that
the opposite edges of the map
should be considered as contiguous or joined.
Related Methods:
Visualizing high dimensional data has been a major topic since many
decades. A large group of methods target high dimensional data with sophisticated
rendering techniques like 3D landscapes, special glyphs, colors, and graphics
etc. Other methods approach the problem by reducing the dimensionality in
a generic way with few assumptions about data type. RPM algorithm belongs
to the latter group. The following is a short list of methods that directly
or indirectly reduce data dimensionality:
 Multidiemensional Scaling: Sammon Mapping, Curvilinear
Component Analysis.
 SelfOrganzing Map.
 Principal Component Analysis/Singulare Value Decomposition.
 Nonlinear Projections: Local Linear Embedding.
 Methods based on physical models: spring embedding model;
force directed placement.
References:
 James Xinzhi Li: Visualization
of High Dimensional Data with Relational Perspective Map. Information Visualization
2004, Vol. 3, No. 1. 4959.
