cut algorithms mentioned in Section 3.1 provide O(log n) approximation algorithms for the flux and minimum quotient separator problems, even in the case where edges and nodes are weighted and where edges are directed.
The main idea to solve this problem is obtaining an approximate solution instead of the sparsest
Yavneh, "A plurality of sparse representations is better than the sparsest
one alone," IEEE Transactions on Information Theory, vol.
Sun, "Recovery of sparsest
signals via lq-minimization," Applied and Computational Harmonic Analysis, vol.
The decorations get less every year, with this year being the sparsest
I can ever remember.
With the sparsest
of musical backing, it's full of dignity, although you almost feel like you're intruding into a private harrowing moment.
The coroner said some of the home's medical records contained "only the sparsest
The method of sparse subspace clustering (SSC) [6, 7] pursues the sparsest
representation for each data point by using the [l.sub.1]-norm regularization.
However, this criterion is not convex, and finding the sparsest
solution of Eq.
Equation (8) seeks the sparsest
one among all the possible solutions of y = [THETA]s.
By enforcing sparsity using l1 norm just like the method L1-SVD, the spatial spectrumis given by finding the sparsest
solution in a redundant basis.
This formulation is equivalent to finding the sparsest
solution, whereas the well known TSVD method tries to find the minimum energy solution ([[??].sub.2] norm minimization).