@proceedings {citation183,
	title = {Deflation Methods for Sparse PCA},
	journal = {Neural Information Processing Systems (NIPS{\textquoteright}08)},
	year = {2008},
	month = {12/2008},
	pages = {1-8},
	address = {Vancouver, Canada},
	abstract = {In analogy to the PCA setting, the sparse PCA problem is often solved by iter- 
atively alternating between two subtasks: cardinality-constrained rank-one vari- 
ance maximization and matrix deflation. While the former has received a great 
deal of attention in the literature, the latter is seldom analyzed and is typically 
borrowed without justification from the PCA context. In this work, we demon- 
strate that the standard PCA deflation procedure is seldom appropriate for the 
sparse PCA setting. To rectify the situation, we first develop several deflation al- 
ternatives better suited to the cardinality-constrained context. We then reformulate 
the sparse PCA optimization problem to explicitly reflect the maximum additional 
variance objective on each round. The result is a generalized deflation procedure 
that typically outperforms more standard techniques on real-world datasets. 

},
	url = {http://books.nips.cc/papers/files/nips21/NIPS2008_0197.pdf},
	author = {Lester Mackey}
}
