Complexity Analysis of the Lasso Regularization Path
نویسندگان
چکیده
The regularization path of the Lasso can be shown to be piecewise linear, making it possible to “follow” and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/ √ ε) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative ε-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.
منابع مشابه
Adaptive multi-penalty regularization based on a generalized Lasso path
For many algorithms, parameter tuning remains a challenging and critical task, which becomes tedious and infeasible in a multi-parameter setting. Multi-penalty regularization, successfully used for solving undetermined sparse regression of problems of unmixing type where signal and noise are additively mixed, is one of such examples. In this paper, we propose a novel algorithmic framework for a...
متن کاملStagewise Lasso
Many statistical machine learning algorithms minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in Lasso or SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalizability, or equivalently between bias and variance. When this tuning parameter changes, a regularization “path” of solutions to the minim...
متن کاملSplit LBI: An Iterative Regularization Path with Structural Sparsity
An iterative regularization path with structural sparsity is proposed in this paper based on variable splitting and the Linearized Bregman Iteration, hence called Split LBI. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model s...
متن کاملStagewise Lasso Stagewise Lasso
Many statistical machine learning algorithms (in regression or classification) minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalibility, or equivalently between bias and variance. When this tuning parameter changes, a regularization “path” of...
متن کاملA Mathematical Analysis of New L-curve to Estimate the Parameters of Regularization in TSVD Method
A new technique to find the optimization parameter in TSVD regularization method is based on a curve which is drawn against the residual norm [5]. Since the TSVD regularization is a method with discrete regularization parameter, then the above-mentioned curve is also discrete. In this paper we present a mathematical analysis of this curve, showing that the curve has L-shaped path very similar t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1205.0079 شماره
صفحات -
تاریخ انتشار 2012