J. Matousek, Embedding finite metric spaces into Euclidean spaces.
Lecture notes, 2000.
Embeddings of finite metrics into normed spaces (l_1 and l_infty).
Includes Bourgain's lemma.
N. Linial, E. London and Yu. Rabinovich,
The geometry of graphs and some of its algorithmic applications,
Combinatorica (1995) 15, pp. 215-245.
Application of Bourgain's lemma to approximating the sparsest cut.
Lots of other embedding results.
D.B. Shmoys. "[Approximation algorithms for] Cut problems and their application to divide-and-conquer". In: Approximation Algorithms for
NP-hard Problems, (D.S. Hochbaum, ed.) PWS, 1997, 192-235.
As above, more algorithmic angle.
Satish Rao, Small Distortion and Volume Preserving Embeddings for Planar
and Euclidean Metrics. Symposium on Computational Geometry 1999: 300-306.
Embeddings of planar graph metrics into l_1.
Approximating the bandwidth via volume respecting embeddings.
Journal of Computer and System Sciences, 60(3), 510--539, June 2000.
Y. Bartal, Probabilistic Approximation of Metric Spaces and its Algorithmic
Applications, Proc. of the 37th Ann. IEEE Symp. on Foundations of Computer Science,
Embeddings of finite metrics into probabilistic trees, part I.
Y. Bartal, Approximating Arbitrary Metrices by Tree Metrics. STOC 1998: 161-168.
Embeddings of finite metrics into probabilistic trees, part II.
S. DasGupta, A. Gupta, An elementary proof of the Johnson-Lindenstrauss lemma,
ICSI Technical Report.
Dimensionality reduction for l_2, simple proof.
S. DasGupta, Learning mixtures of Gaussians , FOCS'99.
An application of JL-lemma.
P. Indyk, Stable Distributions, Pseudorandom Generators, Embeddings and
Data Stream Computation, FOCS'00.
(Strange) dimensionality reduction for l_1, application of embeddings to space-bounded computation.
N. Alon, Y. Matias, M. Szegedy, The space complexity of approximating the
frequency moments, STOC'96.
The original paper on space-bounded computation.