Paper] A Method of Capturing Spatial Audio in Higher Order Ambisonics Exploiting Sparsity in Plain Wave Decomposition

We propose the monotone fused least absolute shrinkage and selection operator (LASSO) model and develop a continuous algorithm for it. The LASSO model is a special case of the fused LASSO model. The LASSO technique improves prediction accuracy and reduces the number of predictors, while the fused LASSO procedure also encourages flatness of the regression predictors. The monotone fused LASSO model describes regression with monotonic constraints better than the fused LASSO model. We adapt Nesterov's fast gradient methods to the monotone fused LASSO model, we give closed-form solutions for each iteration and prove the boundedness of the optimal solution set, and we provide convergence results. Numerical examples are provided and discussed. Our approach can easily be adapted to related problems, such as the monotone regression and the fused LASSO model.

Basis pursuit (BP), basis pursuit deNoising (BPDN), and least absolute shrinkage and selection operator (LASSO) are popular methods for identifying important predictors in the high‐dimensional linear regression model . By definition, when , BP uniquely recovers when and implies (identifiability condition). Furthermore, LASSO can recover the sign of only under a much stronger irrepresentability condition. Meanwhile, it is known that the model selection properties of LASSO can be improved by hard thresholding its estimates. This article supports these findings by proving that thresholded LASSO, thresholded BPDN, and thresholded BP recover the sign of in both the noisy and noiseless cases if and only if is identifiable and large enough. In particular, if X has iid Gaussian entries and the number of predictors grows linearly with the sample size, then these thresholded estimators can recover the sign of when the signal sparsity is asymptotically below the Donoho–Tanner transition curve. This is in contrast to the regular LASSO, which asymptotically, recovers the sign of only when the signal sparsity tends to 0. Numerical experiments show that the identifiability condition, unlike the irrepresentability condition, does not seem to be affected by the structure of the correlations in the X matrix.

29P Plasma tumor-derived small extracellular vesicles microRNAs plus CA-125 objectively detect residual disease risk after surgical debulking in advanced ovarian cancer

Multicollinearity is a violation of assumptions in multiple linear regression analysis that can occur if there is a high correlation between the independent variables. Likewise, the variants of multiple linear regression models such as the Geographically Weighted Regression model (GWR). Multicollinearity causes parameter estimation using the Quadratic Method (QM) unstable and produces a large variance. On the other hand, what is expected in the estimation parameters is an estimate with a minimum variance, even though it is biased. Thus, one way to overcome multicollinearity can be to use biased estimators, such as Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (EN). In RR, the Least Square Method (LSM) coefficient is reduced to zero but it can’t select the independent variable. However, the parameter model obtained from the Ridge Regression is biased, and the variance of the resulting regression coefficients is relatively tiny. In addition, the RR is increasingly difficult to understand if a huge number of independent variables are used. Meanwhile, LASSO is a computational method that uses quadratic programming and can act out the RR principles and perform variable selection. The LASSO method became known after discovering the Least-Angle Regression (LARS) algorithm. The LASSO method can reduce the LSM coefficient to zero to perform variable selection. LASSO also has a weakness, so EN is used. In this article, the performance of the three methods is compared from the mathematical aspect. The performance of each is written as follows, RR is helpful for clustering effects, where collinear features can be selected together; LASSO is proper for feature selection when the dataset has features with poor predictive power and EN combines LASSO and RR, which has the potential to lead to simple and predictive models.

PurposeThe aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT.Methods and MaterialsQuality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC.ResultsEight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values.ConclusionsMultivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

The under-determined problem in Higher Order Ambisonics (HOA) makes it hard for larger accurate reproduction area especially at high frequencies. In this paper, the least absolute shrinkage and selection operator (LASSO) algorithm is used to solve the problem by obtaining the sparse representation of the original plane wave field. The algorithm minimizes the squared error between the original and reproduced microphone signals with constraint on plane wave gains, so as to select a few nonzero plane wave gains for reproduction. Simulation results demonstrate that sparsity-based plane wave field reproduction via LASSO achieves small reproduction errors and outperforms basic decoding. In addition, fewer loudspeakers are active to reproduce the original field and have better performance.

Determination of diphenylamine (DPA) and its nitro derivatives received great attention for storing, deposition and on‐time usage of propellants. Herein, we present a novel and simple method for simultaneous determination of DPA and its nitro derivatives in solid propellants using UV‐Vis spectroscopy and chemometrics techniques. The UV‐Vis spectra of the analytes revealed strong overlap and it was difficult to determine them individually in their mixture without any separation and purification. To tackle the overlapping problem in collected spectra, analysis of first‐order UV‐Vis data was performed using multivariate calibration techniques. In this way, principle component regression (PCR), different modes of partial least square (PLS) and least absolute shrinkage and selection operator (LASSO) have been used for correlating the collected spectra to the concentration of DPAs in synthetic and real samples. The important variables were selected by confining the L1‐norm of the regression coefficients in multivariate model via the shrinkage and selection operator in LASSO approach. The results obtained by LASSO regression technique in this work were superior to those obtained by different modes of PLS algorithm. Moreover, it is shown that LASSO can be used as a reliable variable selection and modeling technique in multivariate calibration studies. Generally, the proposed strategy in this work is simple, non‐destructive, low‐cost and rapid and can be effectively applied for simultaneous determination of DPA and its nitro derivatives in solid propellants.

Orientation selectivity, as an emergent property of neurons in the visual cortex, is of critical importance in the processing of visual information. Characterizing the orientation selectivity based on neuronal firing activities or local field potentials (LFPs) is a hot topic of current research. In this paper, we used cross-frequency coupling and least absolute shrinkage and selection operator (LASSO) to predict the grating orientations in V1 and V4 of two rhesus monkeys. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, which were placed, respectively, in V1 and V4 of two rhesus monkeys performing a selective visual attention task. The phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC) were employed to characterize the cross-frequency coupling of LFPs under sinusoidal grating stimuli with different orientations. Then, a LASSO logistic regression model was constructed to predict the grating orientation based on the strength of PAC and AAC. Moreover, the cross-validation method was used to evaluate the performance of the model. It was found that the average accuracy of the prediction based on the combination of PAC and AAC was 73.9%, which was higher than the predicting accuracy with PAC or AAC separately. In conclusion, a LASSO logistic regression model was introduced in this study, which can predict the grating orientations with relatively high accuracy by using PAC and AAC together. Our results suggest that the principle behind the LASSO model is probably an alternative direction to explore the mechanism for generating orientation selectivity.

Radiomics Analysis of Fat Saturated T2-Weighted MRI Sequences for Prognostic Prediction to Soft-Tissue Sarcoma of the Extremities and Trunk Treated With Neoadjuvant Radiotherapy

In Data Science, we usually encounter High-dimensional data. In this situation, the Classical Regression method usually cannot perform well because it is impossible to include all covariates in the model since the number of a parameter to be estimated is larger than the sample size. Least absolute shrinkage and selection operator (Lasso) method is one of the methods which can deal with this problem. Lasso regression perform the selection of covariates so that only the most influential covariates are used in the model. Unfortunately, most of Lasso method should be performed in CLI Software which is difficult to use for the general user. For this reason, we develop a web application by using Shiny to perform the Lasso method based on GUI which is easier to use. It allows users to analyze high-dimensional data without using programming language. The paper contains an implementation of Lasso Regression using web application on olive pomade oil data.

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography

Spectral variable selection based on least absolute shrinkage and selection operator with ridge-adding homotopy

Regression modeling was used to describe the relationship between the response variable and one or more predictor variables. For a categorical response variable, the logistic regression would be more appropriate for this. It is not uncommon that we encounter a response variable with more than two categories. Hence, we end up modeling a multinomial logistic regression. The estimation of the parameters of the model was done using Maximum Likelihood Estimation (MLE). Furthermore, we used Least Absolute Shrinkage and Selection Operator (LASSO) to further facilitate variable selection in the model. Case studies and simulations were studied using the LASSO model, and are implemented in R. We then implement the LASSO model to analyze the data of senior high school preferences by Public Junior High School and Islamic Junior High School students in Trenggalek Regency, East Java, Indonesia. The influencing factors from the model with LASSO were average score during the 8th semester, school type, father’s occupation, mother’s occupation, mother’s last education, father’s income, mother’s income, and long term plan. The response variable was assumed to follow a multinomial distribution, with three levels. A random error was assumed to follow the normal distribution. There were two predictor variables, and various sample sizes were considered. The results showed that the LASSO estimates are similar to those from parametric estimation.

Analisis waktu tahan hidup (survival analysis) merupakan metode statistik yang digunakan untuk mempelajari faktor-faktor yang memengaruhi waktu hingga terjadinya suatu peristiwa tertentu, seperti kematian, penyakit, atau kekambuhan. Dalam konteks penderita stroke, identifikasi faktor signifikan yang memengaruhi waktu tahan hidup sangat penting untuk mendukung pengambilan keputusan medis dan intervensi klinis. Penelitian ini bertujuan untuk menerapkan metode Least Absolute Shrinkage and Selection Operator (LASSO) dalam menganalisis data waktu tahan hidup penderita stroke. Metode LASSO dipilih karena kemampuannya dalam melakukan seleksi variabel dan regularisasi secara simultan, sehingga mampu menghasilkan model yang sederhana namun akurat. Data yang digunakan mencakup variabel klinis dan demografis penderita stroke, dengan metode Kaplan-Meier digunakan untuk mengestimasi fungsi survival dan regresi Cox-LASSO untuk mengidentifikasi variabel-variabel signifikan. Data pasien yang ada dalam konteks data microarray dan terjadi multikolinearitas pada data pasien stroke. Untuk mengatasi adanya multikolinearitas dan overfitting, maka metode LASSO dapat digunakan untuk mengetahui faktor-faktor yang signifikan berpengaruh terhadap masa hidup penderita stroke, selain menggunakan regresi LASSO terhadap data microarray mengakibatkan tidak diketahuinya variabel bebas yang berkonstribusi terhadap variabel tak bebas. Pada penelitian ini, sebanyak tujuh data pasien digunakan dan dianalisis dengan menggunakan bantuan software R 2.12.1 dengan library lars. Data dianalisis dengan model regresi LASSO dengan struktur fungsi yang telah ada dalam paket R. Data-data yang dicari dalam analisis yaitu nilai estimasi parameter dengan matrik beta, nilai D dan nilai t. Nilai dari D ≤ t dan t ≥ 0 yang berarti batasan dari LASSO tersebut telah terpenuhi. Hasil penelitian menunjukkan bahwa metode LASSO efektif dalam menangani data dengan banyak prediktor serta mampu mengeliminasi variabel yang tidak signifikan, sehingga meningkatkan interpretabilitas model. Temuan ini diharapkan dapat memberikan kontribusi dalam pengelolaan klinis penderita stroke serta pengembangan kebijakan kesehatan berbasis data. Dari hasil penelitian diperoleh model masa tahan hidup pasien adalah dan faktor yang signifikan berpengaruh adalah kondisi awal pemeriksaan dan bagian saraf yang mengalami gangguan.

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. We have extended the PCA-LASSO approach using the bootstrap to estimate the standard errors and confidence intervals of the LASSO coefficient estimates. This method was compared to placing the raw SNP values into the LASSO and the logistic model with individual gene-gene interaction. We demonstrated these methods with the Genetic Analysis Workshop 16 rheumatoid arthritis genome-wide association study data and our results identified a few gene-gene signals. Based on our results, the PCA-LASSO method shows promise in identifying gene-gene interactions, and, at this time we suggest using it with other conventional approaches, such as generalized linear models, to narrow down genetic signals.