2 Rectangular Matrices

[[lecture-main-topic-data]]

We are particularly interested in the d×m matrix

GN(0,1)d×m

Concepts

Concepts

File status type Lectures
bound for the size of an epsilon packing complete 🧮
dimension requirements for gaussian random matrix to be a restricted isometry with high probability complete 🧮
distinguishing is equivalent to double nullspace property complete 🧮
epsilon faithful function complete 💡
epsilon net complete 💡
epsilon net restricted inner product bounds the operator norm in progress 🧮
epsilon packing complete 💡
first moment method in progress 💡
gaussian random matrix transforms vectors into gaussian random vectors complete 🧮
Gram matrix complete 💡
high probability bound for operator norm of difference for Gaussian covariance matrix complete 🧮
high probability bound on singular values of gaussian random matrix complete 🧮
Johnson-Lindenstrauss lemma complete 🧮
joint of gaussian random transform of finite vectors complete 🧮
maximal epsilon packing is also a net complete 🧮
normalized gaussian random matrix preserves geometry in expectation in progress 💡
nullspace property in progress
random matrix in progress 💡
restricted inner product in progress 💡
restricted isometry implies nullspace property complete 🧮
restricted isometry property complete 💡
sparsity complete 💡
unique singular values yield unique outer product components complete 🧮
we can find an epsilon net with the bound for an epsilon packing complete 🧮

Mentions

Mentions

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