Dissimilarity-Based Regularized Learning of Charts

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.

Using Experimentally Calibrated Regularized Stokeslets to Assess Bacterial Flagellar Motility Near a Surface

The presence of a nearby boundary is likely to be important in the life cycle and evolution of motile flagellate bacteria. This has led many authors to employ numerical simulations to model near-surface bacterial motion and compute hydrodynamic boundary effects. A common choice has been the method of images for regularized Stokeslets (MIRS); however, the method requires discretization sizes and regularization parameters that are not specified by any theory. To determine appropriate regularization parameters for given discretization choices in MIRS, we conducted dynamically similar macroscopic experiments and fit the simulations to the data. In the experiments, we measured the torque on cylinders and helices of different wavelengths as they rotated in a viscous fluid at various distances to a boundary. We found that differences between experiments and optimized simulations were less than 5% when using surface discretizations for cylinders and centerline discretizations for helices. Having determined optimal regularization parameters, we used MIRS to simulate an idealized free-swimming bacterium constructed of a cylindrical cell body and a helical flagellum moving near a boundary. We assessed the swimming performance of many bacterial morphologies by computing swimming speed, motor rotation rate, Purcell’s propulsive efficiency, energy cost per swimming distance, and a new metabolic energy cost defined to be the energy cost per body mass per swimming distance. All five measures predicted that the optimal flagellar wavelength is eight times the helical radius independently of body size and surface proximity. Although the measures disagreed on the optimal body size, they all predicted that body size is an important factor in the energy cost of bacterial motility near and far from a surface.

Online regularized pairwise learning with non-i.i.d. observations

In this paper, we consider the online regularized pairwise learning (ORPL) algorithm with least squares loss function for non-independently and identically distribution (non-i.i.d.) observations. We first establish new Bennett’s inequalities for [Formula: see text]-mixing sequence, geometrically [Formula: see text]-mixing sequence, [Formula: see text]-geometrically ergodic Markov chain and uniformly ergodic Markov chain. Then we establish the convergence rates for the last iterate of the ORPL algorithm with the polynomially decaying step sizes and varying regularization parameters for non-i.i.d. observations. These established results in this paper extend the previously known results of ORPL from i.i.d. observations to the case of non-i.i.d. observations, and the established result of ORPL for [Formula: see text]-mixing can be nearly optimal rate of ORPL for i.i.d. observations with [Formula: see text]-norm.

Modified ridge and other regularization criteria: A brief review on meaningful regression models

The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.

STRETCHING THE NET: MULTIDIMENSIONAL REGULARIZATION

This paper derives asymptotic risk (expected loss) results for shrinkage estimators with multidimensional regularization in high-dimensional settings. We introduce a class of multidimensional shrinkage estimators (MuSEs), which includes the elastic net, and show that—as the number of parameters to estimate grows—the empirical loss converges to the oracle-optimal risk. This result holds when the regularization parameters are estimated empirically via cross-validation or Stein’s unbiased risk estimate. To help guide applied researchers in their choice of estimator, we compare the empirical Bayes risk of the lasso, ridge, and elastic net in a spike and normal setting. Of the three estimators, we find that the elastic net performs best when the data are moderately sparse and the lasso performs best when the data are highly sparse. Our analysis suggests that applied researchers who are unsure about the level of sparsity in their data might benefit from using MuSEs such as the elastic net. We exploit these insights to propose a new estimator, the cubic net, and demonstrate through simulations that it outperforms the three other estimators for any sparsity level.

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

Penalized LAD-SCAD Estimator Based on Robust Wrapped Correlation Screening Method for High Dimensional Models

The widely used least absolute deviation (LAD) estimator with the smoothly clipped absolute deviation (SCAD) penalty function (abbreviated as LAD-SCAD) is known to produce corrupt estimates in the presence of outlying observations. The problem becomes more complicated when the number of predictors diverges. To overcome these problems, the LAD-SCAD based on sure independence screening (SIS) technique is put forward. The SIS method uses the rank correlation screening (RCS) algorithm in the pre-screening step and the traditional Pathwise coordinate descent algorithm for computing the sequence of the regularization parameters in the post screening step for onward model selection. It is now evident that the rank correlation is less robust against outliers. Motivated by these inadequacies, we propose to improvise the LAD-SCAD estimator using robust wrapped correlation screening (WCS) method by replacing the rank correlation in the SIS method with robust wrapped correlation. The proposed estimator is denoted as WCS+LAD-SCAD and will be employed for variable selection. The simulation study and real-life data examples show that the proposed procedure produces more efficient results compared to the existing methods.