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基于测地距离不变性的非线性降维算法源码

Instructions for Isomap code, Release 1
---------------------------------------

Author: Josh Tenenbaum ([e-mail])
        [Dijkstra code by Mark Steyvers ([e-mail])]

Date: December 22, 2000

Website for updates: http://isomap.stanford.edu


0. Copyright notice

    Isomap code -- (c) 1998-2000 Josh Tenenbaum

    This code is provided as is, with no guarantees except that 
    bugs are almost surely present.  Published reports of research 
    using this code (or a modified version) should cite the 
    article that describes the algorithm: 

      J. B. Tenenbaum, V. de Silva, J. C. Langford (2000).  A global
      geometric framework for nonlinear dimensionality reduction.  
      Science 290 (5500): 2319-2323, 22 December 2000.  

    Comments and bug reports are welcome.  Email to [e-mail]. 
    I would also appreciate hearing about how you used this code, 
    improvements that you have made to it, or translations into other
    languages.    

    You are free to modify, extend or distribute this code, as long 
    as this copyright notice is included whole and unchanged.  


1. Contents 

This package of Matlab (version 5.3) code implements the Isomap
algorithm of Tenenbaum, de Silva, and Langford (2000) [TdSL].  The
contents of the file "IsomapR1.tar" are:

Isomap.m
IsomapII.m
L2_distance.m 
Readme
dfun.m
dijk.m 
dijkstra.cpp 
dijkstra.dll (Windows binary produced by "mex -O dijkstra")
dijkstra.m 
swiss_roll_data.mat

The data file "swiss_roll_data" contains the input-space coordinates
("X_data") and the known low-dimensional manifold coordinates
("Y_data") for 20,000 data points on the Swiss roll.

Separately available at isomap.stanford.edu is a large data file of 
synthetic face images, "face_data.mat.Z".  The face data file contains
images of 698 faces ("images"), the projections of those faces
onto the first 240 (scaled) principal components ("image_pcs"), 
and the known pose and lighting parameters for each face 
("poses", "lights").  


2. Getting started  (Isomap.m)

Isomap.m implements the basic version of the algorithm described in
[TdSL].  The algorithm takes as input the distances between points
in a high-dimensional observation space, and returns as output their
coordinates in a low-dimensional embedding that best preserves their
intrinsic geodesic distances. 

Try it out on N=1000 points from the "Swiss roll" data set:

>> load swiss_roll_data
>> D = L2_distance(X_data(:,1:1000), X_data(:,1:1000), 1); 

To run Isomap with K = 7 neighbors/point, and produce embeddings
in dimensions 1, 2, ..., 10, type these commands: 

>> options.dims = 1:10;
>> [Y, R, E] = Isomap(D, 'k', 7, options); 

The other options for the code are explained in the header of Isomap.m.  
The only subtle option is "option.comp", which specifies which connected
component to embed in the final step of the algorithm when more than
one component has been detected in the neighborhood graph.  The default
is to embed the largest component. 

This code should work reasonably well for data sets with 1000 or fewer
points.  For larger data sets, consider using the advanced code described
below. 


3. Advanced code  (IsomapII.m)

IsomapII.m implements a more advanced version of the algorithm that
exploits the sparsity of the neighborhood graph in computing shortest-path
distances, and can also exploit the redundancy of the distances in 
constructing a low-dimensional embedding.  

IsomapII uses Dijkstra's algorithm to compute graph distances.
IsomapII works optimally when the file "dijkstra.cpp" (which uses
Fibonacci heap data structures) has been compiled (with the command
"mex -O dijkstra.cpp") to produce "dijkstra.dll".  If IsomapII can't
find a file called dijkstra.dll, it will default to a much slower
Matlab implementation of Dijkstra's algorithm in dijk.m.
Alternatively, setting "option.dijkstra = 0" tells IsomapII to use a
Matlab implementation of Floyd's algorithm (also used in Isomap.m),
which is generally faster than dijk.m for small-to-medium-size data
sets but does not exploit sparsity to achieve better time and space
efficiency for large data sets.  Both dijk.m and the Floyd algorithm
are much much slower than dijkstra.dll, so the latter should be used
if at all possible.  This code package includes a version of
dijkstra.dll suitable for running on Windows platforms.

For very large data sets, it is impractical to store in memory a full
N x N distance matrix, as Isomap produces after step 2 (computing
shortest-path distances), or to calculate its eigenvectors, as Isomap
does in step 3 (constructing a low-dimensional embedding).  However,
in many cases where the data lie on a low-dimensional manifold, the
distances computed in step 2 are heavily redundant and most of them
can be ignored with little effect on the final embedding.  IsomapII
constructs embeddings that, rather than trying to preserve distances
between all pairs of points, preserve only the distances between all
points and a subset of "landmark" points.

The user specifies which points to use as landmarks (in the
options.landmarks field).  Setting "options.landmarks = 1:N" (i.e.
the entire data set) makes step 3 of IsomapII equivalent to classical
multidimensional scaling (MDS); this is just as in Isomap.m, and it is
the default mode for IsomapII.  Choosing a much smaller set of
landmarks (e.g. by sampling randomly, or by using some subset of the
data that is known a priori to be representative) can often be quite a
good approximation.  Try this version of the Swiss roll example with
N=1000 data points but only 50 (random) landmark points:

>> load swiss_roll_data
>> D = L2_distance(X_data(:,1:1000), X_data(:,1:1000), 1); 
>> options.dims = 1:10;
>> options.landmarks = 1:50; 
>> [Y, R, E] = IsomapII(D, 'k', 7, options); 

We do not know of any prior studies of the use of landmark points with
classical MDS, and we have only just begun to explore this approach as
an extension to Isomap.  A more detailed discussion of the use of
landmark points in Isomap and classical MDS will be presented in
Steyvers, de Silva, and Tenenbaum (in preparation, at
http://isomap.stanford.edu).  The use of landmark points in nonmetric
MDS for data visualization is discussed briefly in a paper by Buja,
Swayne, Littman, and Dean ("XGvis: Interactive Data Visualization with
Multidimensional scaling", J. Comp. & Graph. Statistics, 
http://www.research.att.com/areas/stat/xgobi/#xgvis-paper).

To facilitate working with large data sets, IsomapII can take as input
distances in one of three formats:

Mode 1: A full N x N matrix
Mode 2: A sparse N x N matrix
Mode 3: A distance function (see sample: "dfun.m")

Mode 1 is equivalent to Isomap.m (except for the use of dijkstra.dll, 
which will usually be much faster than Isomap.m).  

Mode 2 is designed for cases where the distances between nearby points
are known, but the differences between faraway points are not known.
The sparse input matrix is assumed to contain distances between each
point and a set of neighboring points, from which the graph neighbors
will be chosen using the standard K or epsilon methods.  Any missing
entries in the sparse input matrix are effectively assumed to be
infinite (NOT zero) -- these represents non-neighboring pairs of points.  
Except for the sparsity of the input matrix, Mode 2 does not differ
in any noticeable way from Mode 1. 

Mode 3 is designed for cases where the input-space distances have not
already been computed explicitly.  The user provides a distance
function, which takes as input one argument, i, and returns as output
a row vector containing the input-space distances from all N points to
point i.  A sample distance function, "dfun.m", is provided.  Note
that the distance function may have to use a global variable in order
to encode the information necessary to compute the appropriate
distances.  For example, dfun.m assumes that the coordinates of points
in the high-dimensional input space are encoded in the global variable
X.  It then uses these coordinates to compute Euclidean distances in
input space.  Try this example: 

>> load swiss_roll_data
>> global X
>> X = X_data(:,1:1000); 
>> options.dims = 1:10;
>> options.landmarks = 1:size(X,2); 
>> [Y, R, E] = IsomapII('dfun', 'k', 7, options); 

This should give exactly the same results (subject to numerical error
and sign changes) as the first example in Section 2, but much more
quickly!  Using a distance function rather than a distance matrix
allows IsomapII to handle much larger data sets (we have tested it up
to N=20,000) than the simple Isomap code can handle.  Also, note that
the distance function need not be Euclidean.  Depending on the
application, domain-specific knowledge may be useful for designing a
more sophisticated distance function (as in Fig. 1B of [TdSL]).