Papers Nonparametric Clustering
“Nonparametric Clustering” 태그가 달린 논문 18편 · 필터 해제
Exponentially Consistent Nonparametric Linkage-Based Clustering of Data Sequences
In this paper, we consider nonparametric clustering of $M$ independent and identically distributed (i.i.d.) data sequences generated from {\em unknown} distributions. The distributions of the $M$ data sequences belong to…
ClusteringNonparametric ClusteringA New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules
The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clusterin…
ClusteringGenerative Adversarial NetworkImage GenerationNonparametric ClusteringAn Improved Probability Propagation Algorithm for Density Peak Clustering Based on Natural Nearest Neighborhood
Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy …
ClusteringNonparametric ClusteringA Flexible Bayesian Clustering of Dynamic Subpopulations in Neural Spiking Activity
With advances in neural recording techniques, neuroscientists are now able to record the spiking activity of many hundreds of neurons simultaneously, and new statistical methods are needed to understand the structure of …
ClusteringNonparametric ClusteringDeepDPM: Deep Clustering With an Unknown Number of Clusters
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering…
ClusteringDeep ClusteringDeep Nonparametric ClusteringModel Selection+2Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network
In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in…
ClusteringCross-corpusEmotion RecognitionNonparametric Clustering+1Nonparametric clustering for image segmentation
Image segmentation aims at identifying regions of interest within an image, by grouping pixels according to their properties. This task resembles the statistical one of clustering, yet many standard clustering methods fa…
ClusteringImage SegmentationNonparametric ClusteringSegmentation+2An efficient $k$-means-type algorithm for clustering datasets with incomplete records
The $k$-means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under a…
ClusteringMissing ValuesNonparametric ClusteringIdentifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework involving clustering overfitted \emph{para…
ClusteringNonparametric ClusteringSubgroup Identification and Interpretation with Bayesian Nonparametric Models in Health Care Claims Data
Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient ut…
ClusteringNonparametric ClusteringAdaptive Nonparametric Clustering
This paper presents a new approach to non-parametric cluster analysis called Adaptive Weights Clustering (AWC). The idea is to identify the clustering structure by checking at different points and for different scales on…
ClusteringNonparametric ClusteringSmall-Variance Nonparametric Clustering on the Hypersphere
Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer vision applications, such as scene unders…
ClusteringNonparametric ClusteringScene UnderstandingFast Online Clustering with Randomized Skeleton Sets
We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or …
ClusteringNonparametric ClusteringOnline ClusteringRisk Bounds For Mode Clustering
Density mode clustering is a nonparametric clustering method. The clusters are the basins of attraction of the modes of a density estimator. We study the risk of mode-based clustering. We show that the clustering risk ov…
ClusteringNonparametric ClusteringA Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the …
Clusteringcoreference-resolutionCoreference ResolutionEvent Coreference Resolution+1Deep Learning with Nonparametric Clustering
Clustering is an essential problem in machine learning and data mining. One vital factor that impacts clustering performance is how to learn or design the data representation (or features). Fortunately, recent advances i…
ClusteringDeep LearningDimensionality ReductionNonparametric Clustering+1Scaling Nonparametric Bayesian Inference via Subsample-Annealing
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning…
Bayesian InferenceClusteringNonparametric ClusteringFast nonparametric clustering of structured time-series
In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to…
ClusteringNonparametric ClusteringTime SeriesTime Series Analysis+1