1,365 research outputs found
Relaxed Sparse Eigenvalue Conditions for Sparse Estimation via Non-convex Regularized Regression
Non-convex regularizers usually improve the performance of sparse estimation
in practice. To prove this fact, we study the conditions of sparse estimations
for the sharp concave regularizers which are a general family of non-convex
regularizers including many existing regularizers. For the global solutions of
the regularized regression, our sparse eigenvalue based conditions are weaker
than that of L1-regularization for parameter estimation and sparseness
estimation. For the approximate global and approximate stationary (AGAS)
solutions, almost the same conditions are also enough. We show that the desired
AGAS solutions can be obtained by coordinate descent (CD) based methods.
Finally, we perform some experiments to show the performance of CD methods on
giving AGAS solutions and the degree of weakness of the estimation conditions
required by the sharp concave regularizers
Sampling Requirements for Stable Autoregressive Estimation
We consider the problem of estimating the parameters of a linear univariate
autoregressive model with sub-Gaussian innovations from a limited sequence of
consecutive observations. Assuming that the parameters are compressible, we
analyze the performance of the -regularized least squares as well as a
greedy estimator of the parameters and characterize the sampling trade-offs
required for stable recovery in the non-asymptotic regime. In particular, we
show that for a fixed sparsity level, stable recovery of AR parameters is
possible when the number of samples scale sub-linearly with the AR order. Our
results improve over existing sampling complexity requirements in AR estimation
using the LASSO, when the sparsity level scales faster than the square root of
the model order. We further derive sufficient conditions on the sparsity level
that guarantee the minimax optimality of the -regularized least squares
estimate. Applying these techniques to simulated data as well as real-world
datasets from crude oil prices and traffic speed data confirm our predicted
theoretical performance gains in terms of estimation accuracy and model
selection
The Benefit of Group Sparsity in Group Inference with De-biased Scaled Group Lasso
We study confidence regions and approximate chi-squared tests for variable
groups in high-dimensional linear regression. When the size of the group is
small, low-dimensional projection estimators for individual coefficients can be
directly used to construct efficient confidence regions and p-values for the
group. However, the existing analyses of low-dimensional projection estimators
do not directly carry through for chi-squared-based inference of a large group
of variables without inflating the sample size by a factor of the group size.
We propose to de-bias a scaled group Lasso for chi-squared-based statistical
inference for potentially very large groups of variables. We prove that the
proposed methods capture the benefit of group sparsity under proper conditions,
for statistical inference of the noise level and variable groups, large and
small. Such benefit is especially strong when the group size is large.Comment: 39 Pages, 2 Figures, 1 Tabl
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
We propose a DC proximal Newton algorithm for solving nonconvex regularized
sparse learning problems in high dimensions. Our proposed algorithm integrates
the proximal Newton algorithm with multi-stage convex relaxation based on the
difference of convex (DC) programming, and enjoys both strong computational and
statistical guarantees. Specifically, by leveraging a sophisticated
characterization of sparse modeling structures/assumptions (i.e., local
restricted strong convexity and Hessian smoothness), we prove that within each
stage of convex relaxation, our proposed algorithm achieves (local) quadratic
convergence, and eventually obtains a sparse approximate local optimum with
optimal statistical properties after only a few convex relaxations. Numerical
experiments are provided to support our theory.Comment: 36 pages, 5 figures, 1 table, Accepted at NIPS 201
Convex Formulation for Regularized Estimation of Structural Equation Models
Path analysis is a model class of structural equation modeling (SEM), which
it describes causal relations among measured variables in the form of a
multiple linear regression. This paper presents two estimation formulations,
one each for confirmatory and exploratory SEM, where a zero pattern of the
estimated path coefficient matrix can explain a causality structure of the
variables. The original nonlinear equality constraints of the model parameters
were relaxed to an inequality, allowing the transformation of the original
problem into a convex framework. A regularized estimation formulation was then
proposed for exploratory SEM using an l1-type penalty of the path coefficient
matrix. Under a condition on problem parameters, our optimal solution is low
rank and provides a useful solution to the original problem. Proximal
algorithms were applied to solve our convex programs in a large-scale setting.
The performance of this approach was demonstrated in both simulated and real
data sets, and in comparison with an existing method. When applied to two real
application results (learning causality among climate variables in Thailand and
examining connectivity differences in autism patients using fMRI time series
from ABIDE data sets) the findings could explain known relationships among
environmental variables and discern known and new brain connectivity
differences, respectively.Comment: 24 pages, 19 figure
Pairwise Difference Estimation of High Dimensional Partially Linear Model
This paper proposes a regularized pairwise difference approach for estimating
the linear component coefficient in a partially linear model, with consistency
and exact rates of convergence obtained in high dimensions under mild scaling
requirements. Our analysis reveals interesting features such as (i) the
bandwidth parameter automatically adapts to the model and is actually
tuning-insensitive; and (ii) the procedure could even maintain fast rate of
convergence for -H\"older class of . Simulation studies
show the advantage of the proposed method, and application of our approach to a
brain imaging data reveals some biological patterns which fail to be recovered
using competing methods.Comment: 28 page
Regularized Partial Least Squares with an Application to NMR Spectroscopy
High-dimensional data common in genomics, proteomics, and chemometrics often
contains complicated correlation structures. Recently, partial least squares
(PLS) and Sparse PLS methods have gained attention in these areas as dimension
reduction techniques in the context of supervised data analysis. We introduce a
framework for Regularized PLS by solving a relaxation of the SIMPLS
optimization problem with penalties on the PLS loadings vectors. Our approach
enjoys many advantages including flexibility, general penalties, easy
interpretation of results, and fast computation in high-dimensional settings.
We also outline extensions of our methods leading to novel methods for
Non-negative PLS and Generalized PLS, an adaption of PLS for structured data.
We demonstrate the utility of our methods through simulations and a case study
on proton Nuclear Magnetic Resonance (NMR) spectroscopy data
Sorted Concave Penalized Regression
The Lasso is biased. Concave penalized least squares estimation (PLSE) takes
advantage of signal strength to reduce this bias, leading to sharper error
bounds in prediction, coefficient estimation and variable selection. For
prediction and estimation, the bias of the Lasso can be also reduced by taking
a smaller penalty level than what selection consistency requires, but such
smaller penalty level depends on the sparsity of the true coefficient vector.
The sorted L1 penalized estimation (Slope) was proposed for adaptation to such
smaller penalty levels. However, the advantages of concave PLSE and Slope do
not subsume each other. We propose sorted concave penalized estimation to
combine the advantages of concave and sorted penalizations. We prove that
sorted concave penalties adaptively choose the smaller penalty level and at the
same time benefits from signal strength, especially when a significant
proportion of signals are stronger than the corresponding adaptively selected
penalty levels. A local convex approximation, which extends the local linear
and quadratic approximations to sorted concave penalties, is developed to
facilitate the computation of sorted concave PLSE and proven to possess desired
prediction and estimation error bounds. We carry out a unified treatment of
penalty functions in a general optimization setting, including the penalty
levels and concavity of the above mentioned sorted penalties and mixed
penalties motivated by Bayesian considerations. Our analysis of prediction and
estimation errors requires the restricted eigenvalue condition on the design,
not beyond, and provides selection consistency under a required minimum signal
strength condition in addition. Thus, our results also sharpens existing
results on concave PLSE by removing the upper sparse eigenvalue component of
the sparse Riesz condition
Calibrated zero-norm regularized LS estimator for high-dimensional error-in-variables regression
This paper is concerned with high-dimensional error-in-variables regression
that aims at identifying a small number of important interpretable factors for
corrupted data from many applications where measurement errors or missing data
can not be ignored. Motivated by CoCoLasso due to Datta and Zou \cite{Datta16}
and the advantage of the zero-norm regularized LS estimator over Lasso for
clean data, we propose a calibrated zero-norm regularized LS (CaZnRLS)
estimator by constructing a calibrated least squares loss with a positive
definite projection of an unbiased surrogate for the covariance matrix of
covariates, and use the multi-stage convex relaxation approach to compute the
CaZnRLS estimator. Under a restricted eigenvalue condition on the true matrix
of covariates, we derive the -error bound of every iterate and
establish the decreasing of the error bound sequence, and the sign consistency
of the iterates after finite steps. The statistical guarantees are also
provided for the CaZnRLS estimator under two types of measurement errors.
Numerical comparisons with CoCoLasso and NCL (the nonconvex Lasso proposed by
Poh and Wainwright \cite{Loh11}) demonstrate that CaZnRLS not only has the
comparable or even better relative RSME but also has the least number of
incorrect predictors identified
Support recovery without incoherence: A case for nonconvex regularization
We demonstrate that the primal-dual witness proof method may be used to
establish variable selection consistency and -bounds for sparse
regression problems, even when the loss function and/or regularizer are
nonconvex. Using this method, we derive two theorems concerning support
recovery and -guarantees for the regression estimator in a general
setting. Our results provide rigorous theoretical justification for the use of
nonconvex regularization: For certain nonconvex regularizers with vanishing
derivative away from the origin, support recovery consistency may be guaranteed
without requiring the typical incoherence conditions present in -based
methods. We then derive several corollaries that illustrate the wide
applicability of our method to analyzing composite objective functions
involving losses such as least squares, nonconvex modified least squares for
errors-in variables linear regression, the negative log likelihood for
generalized linear models, and the graphical Lasso. We conclude with empirical
studies to corroborate our theoretical predictions.Comment: 51 pages, 13 figure
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