You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets, via non-parametric kernel smoothing. We provide generalization bounds for dictionary learning using smooth sparse coding and show how the sample complexity depends on the L1 norm of kernel function used. Furthermore, we propose using marginal regression for obtaining sparse codes, which significantly improves the speed and allows one to scale to large dictionary sizes easily. We demonstrate the advantages of the proposed approach, both in terms of accuracy and speed by extensive experimentation on several real data sets. In addition, we demonstrate how the proposed approach could be used for improving semi-supervised sparse coding.

Cited Abstract(s):
[1]: 

[2]: This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point x on the manifold can be locally approximated by a linear combination of its nearby anchor points, and the linear weights become its local coordinate coding. We show that a high dimensional nonlinear function can be approximated by a global linear function with respect to this coding scheme, and the approximation quality is ensured by the locality of such coding. The method turns a difficult nonlinear learning problem into a simple global linear learning problem, which overcomes some drawbacks of traditional local learning methods.

[3]: A kernel smoother is an intuitive estimate of a regression function or conditional expectation; at each point xO the estimate of E(YIxo) is a weighted mean of the sample Yi, with observations close to xo receiving the largest weights. Unfortunately this simplicity has flaws. At the boundary of the predictor space, the kernel neighborhood is asymmetric and the estimate may have substantial bias. Bias can be a problem in the interior as well if the predictors are nonuniform or if the regression function has substantial curvature. These problems are particularly severe when the predictors are multidimensional. A variety of kernel modifications have been proposed to provide ap- proximate and asymptotic adjustment for these biases. Such methods generally place substantial restrictions on the regression problems that can be considered; in unfavorable situations, they can perform very poorly. Moreover, the necessary modifications are very difficult to imple- ment in the multidimensional case. Local regression smoothers fit low-order polynomials in x locally at xO, and the estimate of f(xo) is taken from the fitted polynomial at xO. They automatically, intuitively and simultaneously adjust for both the biases above to the given order and generalize naturally to the multidi- mensional case. They also provide natural estimates for the derivatives of f, an approach more attractive than using higher-order kernel functions for the same purpose.

[4]: When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We usel1-penalized estimators like the Lasso or the Adaptive Lasso which can simultaneously do parameter estimation and model selection. We show that for a time-course of high-dimensional lin- ear models the convergence rates of the Lasso and of the Adaptive Lasso can be improved by combining the different time-points in a suitable way. Moreover, the Adaptive Lasso still enjoys oracle properties and consistent variable selection. The finite sample properties of the proposed methods are illustrated on simulated data and on a real problem of motif finding in DNA sequences. AMS 2000 subject classifications: Primary 62J07; secondary 62J99, 62H12.
Our approach is related to the local regression method [1] [3] . More recent related work is [4] that uses smoothing techniques in high-dimensional lasso regression in the context of temporal data. Another recent approach proposed by [2] achieves code locality by approximating data points using a linear combination of nearby basis points. The main difference is that traditional local regression techniques do not involve basis learning. In this work, we propose to learn the basis or dictionary along with the regression coefficients locally.