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In the second approach, we employ random projections to project the original data into a space of dimension d (where d can be chosen by the user).
Random projections are used to project the input data into a lower dimensional space (closely preserving distances).
Normally, compressed sensing uses random projections as measurements.
We also show that random projections can be used to effectively sample the permutation.
(ii) These random projections cannot change after the linear optical system is fixed.
The possibility of considering random projections to identify probability distributions belonging to parametric families is explored.
In this talk, I will introduce a fundamentally new approach based on random projections and combinatorial optimization.
This is done by bounding the eigenvalues of sub-matrices, as well as an empirical comparison with random projections.
This key observation has led to the design of sensing systems that can directly capture the information using far fewer samples typically acquired via random projections.
With his former student Li-Yang Tan (PhD '14), he will use random projections to prove lower bounds on Boolean circuits.
Random projections, and more generally sketching algorithms, have been used to great success to mitigate the curse of dimensionality in prediction problems in machine learning.
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