Nonparametric Algorithm Research Articles

APPLICATION OF THE ALGORITHM OF PARAMETRIC AND NON-PARAMETRIC CONFIDENCE INTERVALS IN PRE-PROCESSING IMAGE DATA

Digital image processing and enhancement is one of the most important and frequently used issues in many fields of image processing. When handling images or sending them over a particular channel, they are subject to certain noise and require filtering methods. In this paper, the parametric confidence interval algorithm was compared to the nonparametric confidence interval algorithm for processing the noisy images. The results showed that a nonparametric confidence interval algorithm is better at defining the external parameters of an image in terms of noise elimination and enhancement landmarks.

A comparative analysis of prediction problems utilizing Interval type-2 fuzzy and machine learning models

This paper compares the effectiveness of Interval Type 2 Fuzzy Logic (IT2FL) and Machine Learning (ML) models in addressing real-world challenges. It employs four non-parametric ML algorithms (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), (Random Forest (RF) and Classification and Regression Tree (CART)) and evaluates their performance against IT2FL using parameters generated through Gaussian membership functions. Initially, the IT2FL algorithm preprocesses the dataset by predicting missing values, leveraging IT2F membership to optimize decision-making and mitigate uncertainties. The study assesses the predictive performance, robustness, and interpretability of IT2F-ML models, analyzing datasets from cardiovascular disease patients to predict shock levels. After transforming the dataset using IT2FL, it is divided into 60% training and 40% testing sets to train and test four ML algorithms, aimed at predicting shock levels in patients. The models’ performance is evaluated using various metrics, demonstrating the superior learning enhancement and performance of the IT2F-SVR method compared to other predictive approaches on the same dataset. Moreso, it is observed that the integration of the IT2-FL algorithm with machine learning models offers a promising approach for addressing data incompleteness and improving prediction accuracy. Furthermore, the study provides valuable guidance for researchers and practitioners in selecting suitable methodologies for prediction tasks.

Leaf Identification in High-Density LiDAR-RGB Data

Abstract. Leaf detection through automated segmentation of 3D data is becoming a crucial technique in many applications of digital agriculture. Some 3D segmentation techniques that can be mentioned are based on normal differences and median normalised vector growth. However, applying these approaches to high canopy density data remains challenging. In this study, we propose a processing flow for leaf detection in high canopy density LiDAR-RGB point clouds. First, a noise removal technique inspired by Moving Least Squares (MLS) was applied to the LiDAR point cloud, and a RGB colour was assigned to each point by combining computer vision and photogrammetric methods. Moreover, once the data were suitable for leaf detection, the branches were filtered using the Statistical Outlier Removal (SOR) filter based on an analysis of the statistical behaviour of the neighbourhood. Afterwards, an unsupervised DBSCAN (Density-Based Clustering Non-Parametric Algorithm) method was used to segment similar points. Finally, the points within each cluster were identified as leaf or non-leaf using the RGB values implemented by our method; ground points were filtered out using a maximum height threshold. As a result, the leaf, non-leaf, and ground point identifiers were correct in 98.9% of cases, with the branch filtering technique SOR proving effectiveness in removing branches with low information loss and without additional complex point densification steps in reconstruction. This SOR-based solution overcomes major challenges in semantic segmentation (leaves and branches) in high-density data and potentially contributes to precision agriculture.

Development of a quantitative prediction algorithm for human cord blood-derived CD34+ hematopoietic stem-progenitor cells using parametric and non-parametric machine learning models

The transplantation of CD34+ hematopoietic stem-progenitor cells (HSPCs) derived from cord blood serves as the standard treatment for selected hematological, oncological, metabolic, and immunodeficiency disorders, of which the dose is pivotal to the clinical outcome. Based on numerous maternal and neonatal parameters, we evaluated the predictive power of mathematical pipelines to the proportion of CD34+ cells in the final cryopreserved cord blood product adopting both parametric and non-parametric algorithms. Twenty-four predictor variables associated with the cord blood processing of 802 processed cord blood units randomly sampled in 2020–2022 were retrieved and analyzed. Prediction models were developed by adopting the parametric (multivariate linear regression) and non-parametric (random forest and back propagation neural network) statistical models to investigate the data patterns for determining the single outcome (i.e., the proportion of CD34+ cells). The multivariate linear regression model produced the lowest root-mean-square deviation (0.0982). However, the model created by the back propagation neural network produced the highest median absolute deviation (0.0689) and predictive power (56.99%) in comparison to the random forest and multivariate linear regression. The predictive model depending on a combination of continuous and discrete maternal with neonatal parameters associated with cord blood processing can predict the CD34+ dose in the final product for clinical utilization. The back propagation neural network algorithm produces a model with the highest predictive power which can be widely applied to assisting cell banks for optimal cord blood unit selection to ensure the highest chance of transplantation success.

Application of Sentinel-2 Level-2A images for monitoring water surface in reservoirs in the semiarid region of Pernambuco — Brazil

Remote sensing techniques offer effective and efficient alternatives for observing the spatiotemporal dynamics of surface water in reservoirs. This paper aimed to analyze the applicability of Sentinel-2 Level-2A satellite images from 2016 to 2024 for mapping and monitoring the extent of water surfaces in reservoirs in the Sertão region of Pernambuco state. An automatic, unsupervised, and non-parametric algorithm was employed, combining water indices with reflectance bands of optical images to identify water pixels. The results were compared with two datasets: in situ monitoring and MapBiomas. Issues with optical images affected by clouds over the reservoir and errors in classifying water pixels were noted. Generally, the algorithm tended to underestimate the extent of the water surface due to difficulty detecting water pixels at the edges of the reservoirs. To mitigate this issue, an artificial neural network (ANN) was applied to correct the underestimation bias. The bias correction improved the performance of the metrics when the size and representativeness of the calibration sample were sufficient for training and building the ANN model.

Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. This study aims to select the most suitable model to estimate tropical forest aboveground biomass in Vietnam, helping to manage and monitor changes in biomass at regional and local scales. The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. The results indicated that all models had good estimation performance with R2 ranging from 0.615 to 0.754. For RF, MLR, and XGBoost, vegetation indices contributed the highest model weights, occupying 77.71% – 92.48%. For ANN and SVM, textural and topographic features were the majority of the model weights (73.74 – 96.36%). The RF model performed the best using 59 variables (R2 = 0.754, MAE = 78.5 Mg·ha−1, and %RMSE = 13.57%) and ten variables (R2 = 0.745, MAE = 85.8 Mg·ha−1, and %RMSE = 16.17%). The biomass map using the RF and ten variables achieved a good degree of fitting of 0.76, so it was suitable for managing and monitoring forest biomass in Vietnam. The results indicated a sharp decrease in the areas of dense and very dense forests from 2013 to 2021 and a gradual increase in 2023.

Bayesian clustering with uncertain data.

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc.

Nonparametric data-driven learning algorithms for multilocation inventory systems

We study a multilocation inventory system with unknown demand distribution using a nonparametric approach. The system consists of multiple distribution centers and customer locations, where products are shipped from the distribution centers to fulfill customer demands. We propose a novel algorithm, DMLI, for adaptive inventory management. Under specific conditions, we establish that the average expected T-period regret of DMLI converges to the optimal rate of O(1/T).

Inverse set estimation and inversion of simultaneous confidence intervals.

Motivated by the questions of risk assessment in climatology (temperature change in North America) and medicine (impact of statin usage and coronavirus disease 2019 on hospitalized patients), we address the problem of estimating the set in the domain of a function whose image equals a predefined subset of the real line. Existing methods require strict assumptions. We generalize the estimation of such sets to dense and nondense domains with protection against inflated Type I error in exploratory data analysis. This is achieved by proving that confidence sets of multiple upper, lower, or interval sets can be simultaneously constructed with the desired confidence nonasymptotically through inverting simultaneous confidence intervals. Nonparametric bootstrap algorithm and code are provided.

Innovative educational and research center for monitoring forest resources of Siberia based on laser and microwave aerospace imaging

The possibilities of creating an innovative educational and scientific center for monitoring forest resources in Siberia on the basis of the Department of Space Facilities and Technologies of the Siberian State University of Science and Technology named after Mikhail Fedorovich Reshetnev are discussed, with the aim of training highly qualified engineering personnel and conducting promising scientific research in the field of monitoring, modeling, forecasting and management of forest resources. Methodological solutions and algorithms for three-dimensional modeling of forest structure and dynamics based on laser scanning data, digital aerial and space photography are proposed. These methods contribute to operational monitoring and can significantly reduce the cost of monitoring the condition and use of forest resources over the vast territory of Siberia. Remote sensing data is presented in the form of a geotransformed database and digital photo map, compatible in formats with computer-aided design systems and with the main geographic information systems – ArcView, ArcINFO, MapINFO. The innovative monitoring center will be used for operational state control and monitoring of forest management, the state of forest lands, forest management and forest inventory, solving problems of ecology and environmental management, geoecology, formation of a forest resource inventory, aerospace methods for studying natural resources and territories, information technology. Solving these problems will allow for the training of highly qualified specialists. The center's specialists plan to create information technologies for remote sensing of natural objects with the aim of import substitution of foreign software products. The main scientific directions of the created center: development and research of methods for system analysis of large-scale multidimensional remote sensing data based on nonparametric decision-making algorithms and parallel computing technologies; testing hypotheses about the distributions of large-volume remote sensing data based on nonparametric nuclear-type pattern recognition algorithms; detection of compact groups of large-volume remote sensing data corresponding to unimodal fragments of the joint probability density of multivariate random variables.

The optimal measurement period of actigraphy for circadian rhythm in relation to adiposity: A retrospective case-control study

BackgroundThis study focused on the relationship between adiposity and Rest-Activity Rhythms (RAR), utilizing both parametric cosine-based models and non-parametric algorithms. The emphasis was on the impact of varying measurement periods (7–28 days) on this relationship. MethodsWe retrieved actigraphy data from two datasets, encompassing a diverse cohort recruited from an obesity outpatient clinic and a workplace health promotion program. Participants were required to wear a research-grade wrist actigraphy device continuously for a minimum of four weeks. The final dataset included 115 individuals (mean age 40.7 ± 9.5 years, 51 % female). We employed both parametric and non-parametric methods to quantify RAR using six standard variables. Additionally, the study evaluated the correlations between three key adiposity indices — Body Mass Index (BMI), Visceral Adipose Tissue (VAT) area, and Body Fat Percentage (BF%) — and circadian rhythm indicators, controlling for factors like physical activity, age, and gender. ResultsThe obesity group displayed a significantly lower relative amplitude (RA) as per non-parametric algorithm findings, with a decreased amplitude noted in the parametric algorithm analysis, in comparison to the overweight and control groups. The relationship between circadian rhythm indicators and adiposity metrics over 7- to 28-day periods was examined. A notable negative correlation was observed between RA and both BMI and VAT, while correlation coefficients between adiposity indicators and non-parametric circadian parameters increased with extended durations of actigraphy data. Specifically, RA over a 28-day period was significantly correlated with BF%, a trend not seen in the 7-day measurement (p = 0.094) in multivariate linear regression. The strength of the correlation between BF% and 28-day RA was more pronounced than that in the 7-day period (p = 0.044). However, replacing RA with amplitude as per parametric cosinor fitting yielded no significant correlations for any of the measurement periods. ConclusionThe study concludes that a 28-day measurement period more effectively captures the link between disrupted circadian rhythms and adiposity. Non-parametric algorithms, in particular, were more effective in characterizing disrupted circadian rhythms, especially when extending the measurement period beyond the standard 7 days.

Reconciling shared versus context-specific information in a neural network model of latent causes

It has been proposed that, when processing a stream of events, humans divide their experiences in terms of inferred latent causes (LCs) to support context-dependent learning. However, when shared structure is present across contexts, it is still unclear how the “splitting” of LCs and learning of shared structure can be simultaneously achieved. Here, we present the Latent Cause Network (LCNet), a neural network model of LC inference. Through learning, it naturally stores structure that is shared across tasks in the network weights. Additionally, it represents context-specific structure using a context module, controlled by a Bayesian nonparametric inference algorithm, which assigns a unique context vector for each inferred LC. Across three simulations, we found that LCNet could (1) extract shared structure across LCs in a function learning task while avoiding catastrophic interference, (2) capture human data on curriculum effects in schema learning, and (3) infer the underlying event structure when processing naturalistic videos of daily events. Overall, these results demonstrate a computationally feasible approach to reconciling shared structure and context-specific structure in a model of LCs that is scalable from laboratory experiment settings to naturalistic settings.

Two-stage transfer learning-based nonparametric system identification with Gaussian process regression

Most system identification methods ignore correlations between different identification tasks and do not make full use of historical models when identifying a new process. In this paper, a two-stage transfer learning-based nonparametric system identification method is proposed to improve model accuracy and to reduce identification costs. First, to overcome the transfer capability limitations of the discrete-time model, the target process is modeled as a continuous-time impulse response (IR) function, which consists of a transfer model part and a residual model part, which is regarded as a zero-mean Gaussian process (GP). Then, by utilizing the process geometrical characteristics, the IR functions are classified into three IR domains, and transformation functions within domain or between domains are designed to learn the relationship between the source process model and the target process model. Finally, a two-stage nonparametric identification algorithm based on GP regression is developed: The first stage is performed to select the appropriate type of transformation function through the weighted-derivative dynamical time warping technique, and the second stage is conducted to estimate the transfer model and residual model by using the empirical Bayes approach. Three case studies are conducted to validate the superiority of the proposed identification method.

Interpretable data-driven prediction methods for atmospheric chloride deposition rate

Precise estimation of atmospheric chloride deposition is crucial in environmental science, providing valuable insights into atmospheric pollution, metal corrosion, and the hydrologic cycle. The distribution of atmospheric chloride deposition involves multiple processes of production, transportation, and deposition, making it a problem with well-defined physical mechanisms. Although numerous parametric models have been proposed for atmospheric chloride distribution prediction, they present challenges in terms of generalizability and prediction accuracy due to simplifications of the holistic process. In contrast, non-parametric machine learning algorithms can analyze complex issues and have good prediction performance, but they are used less often in atmospheric chloride deposition modeling. Therefore, we develop a genetic algorithm optimized quantile regression forest (GA-QRF) model, incorporating environmental variables associated with the holistic deposition process to predict the distribution of atmospheric chloride deposition. QRF is an ensemble learning method known for delivering precise and interpretable predictions, coupled with effective uncertainty estimation. The proposed model results achieved an R2 value of 0.935 for median prediction and 98.2% coverage of the intervals for uncertainty quantification on a dataset collected from six countries. In addition, in order to verify the consistency of the black-box machine learning model with the domain knowledge, several interpretation methods are used to deconstruct the proposed model. Considering the spatial variability of atmospheric chloride deposition, the proposed model was further used to create deposition distribution maps.

Assessing above-ground biomass in reforested urban landscapes using machine learning and remotely sensed data

ABSTRACT Urban reforestation mitigates climate change by sequestering carbon, but quantifying carbon gains requires accurate aboveground biomass estimation. This study estimated carbon sequestration in a reforested urban landscape using PlanetScope, Sentinel-1A, Sentinel-2A, SRTM data, and field measurements. Non-parametric machine learning algorithms (k-nearest neighbor, support vector machines, extreme gradient boosting, random forests) with 39 predictor features generated aboveground biomass density maps. The extreme gradient boosting model performed best, predicting 4.1–286.5t ha-1 aboveground biomass, demonstrating its effectiveness for modeling reforested biomass with multi-source data. Findings highlight extreme gradient boosting’s promise for urban biomass estimation, the importance of multi-source data, and machine learning’s potential in addressing environmental challenges like climate change.

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Data-driven atmospheric corrosion prediction model for alloys based on a two-stage machine learning approach

Alloy material corrosion is a comprehensive problem affected by multi-dimensional factors, including the time, environment, and material element. Its corrosion prediction has been facing the challenges of complex mechanism and small sample size. However, parametric methods are limited in terms of modeling accuracy and generalization performance for high-dimensional feature problems. Non-parametric machine learning algorithms, while having excellent modeling capabilities, need to be optimized in conjunction with the structure of the corrosion problem. Therefore, we develop a two-stage hybrid intelligent machine learning model for improving alloy corrosion prediction accuracy. The model takes the time, environment, and material information of the alloy corrosion process as inputs and the corrosion rate as output. The first stage of the model utilizes tree ensemble learning (TEL) models for initial prediction modeling and feature importance knowledge mining. In the second stage, the neural network (NN) is used to enhance the fusion of the multi-model prediction outputs, and it is optimized using feature importance modification (FIM). The proposed TEL-FIMNN model shows considerable performance improvement over the TEL and ANN models on real low-alloy steel corrosion dataset. Compared to other advanced multi-model fusion methods such as linear regression (LR), support vector regression (SVR), random forest (RF), and Bayesian model averaging (BMA), the proposed model also shows superior results. The TEL-FIMNN model provide a new structure from the perspectives of knowledge mining and fusion enhancement, which has good potential for multi-dimensional corrosion prediction.

Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.

Refining the 30-day emergency department visit after initial chemotherapy administration A-PROACCT model.

e23253 Background: This study attempted to address limitations in the A-PROACCT model developed by Stein et al. (JOP, 2023) to predict the rate of acute care events within 30 days (ACE30) of initial chemotherapy administration (ICA). The four most important predictors were drug category (immunotherapy), cancer type (gastrointestinal), age group ( < 40), and BMI category (underweight). Only linear relationships were considered. For patients for whom an ACE30 was predicted by their first and second models, only 0.18 and 0.23 actually had an ACE30, a low positive predictive value (PPV). We attempted to improve the PPV with advanced modeling techniques. Methods: Using Orlando Health data between February of 2012 and April 2021, random sampling was used to split the dataset into training (10,838) and validation (4645). For the training data, resampling was employed to obtain a balanced quota, equal observations followed by an ACE30 and not followed by an ACE30. We included only variables used in Stein (drug category, age group, ED visits and hospitalizations in the prior year, cancer type, insurance category, number of anti-cancer agents, race, and BMI category). Logistic regression with L1-penalty, XGBoost (a non-linear nonparametric tree-based algorithm to account for nonlinear relationships), and artificial neural networks were used to develop and evaluate predictive models. Different sampling methods (bootstrap and SMOTE) as well as cuff-off thresholds for high-risk groups were tested. Results: Based on evaluation with validation data set, the best performing approach was an XGBoost model with SMOTE resampling in the training data. The four most important predictors were ED visits in the prior year, payor category (self-insured), cancer type (genitourinary) and hospitalization in past year. This model reported a PPV of 0.27. Many other combinations of methodologies described above were performed, and the PPV varied between 0.19 to 0.27. Of the 282 ICAs identified as high-risk by the best model, 76 (27.0%) had an ACE30. Of the 4346 ICAs identified as low-risk, 533 (12.3%) had an ACE30. The differences between high risk and low risk ACE30 rates were statistically significant (p < 0.0001). In comparison, using Stein et al. models with our data reported a PPV of 0.27 and 0.25 which are basically at the same level of our best trained model. Conclusions: Using the variables included in A-PROACCT and a variety of machine learning models, advanced sampling methodologies and threshold cut-off limits, the best results were similar to those obtained using basic logistic regression. This suggests improvement in predicting acute care events following chemotherapy administration need to incorporate more extensive multimodal data, such as vital signs, performance status, patient reported outcomes, socioeconomic factors, laboratory and radiology results, remote patient monitoring and wearable device data.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.