14 Result(s)
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Article
Expectation propagation learning of finite multivariate Beta mixture models and applications
Clustering is an attractive method to handle large-scale data which are explosively generated through digitization. This approach is specifically appropriate when labeling is very costly. In this paper, we con...
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Chapter and Conference Paper
Stochastic Expectation Propagation Learning for Unsupervised Feature Selection
We introduce a statistical procedure for the simultaneous clustering and feature selection of positive vectors. The proposed method is based on well-principled infinite generalized inverted Dirichlet (GID) mix...
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Chapter and Conference Paper
Entropy-Based Variational Learning of Finite Generalized Inverted Dirichlet Mixture Model
Mixture models are considered as a powerful approach for modeling complex data in an unsupervised manner. In this paper, we introduce a finite generalized inverted Dirichlet mixture model for semi-bounded data...
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Chapter and Conference Paper
Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis
Recently, axial data (i.e. the observations are axes of direction) have been involved with various fields ranging from blind speech separation to gene expression data clustering. In this paper, axial data mode...
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Chapter and Conference Paper
Unsupervised Variational Learning of Finite Generalized Inverted Dirichlet Mixture Models with Feature Selection and Component Splitting
Variational learning of mixture models has proved to be effective i...
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Chapter and Conference Paper
Efficient Audio-Visual Speaker Recognition via Deep Heterogeneous Feature Fusion
Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mai...
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Chapter and Conference Paper
Efficient Cross-modal Retrieval via Discriminative Deep Correspondence Model
Cross-modal retrieval has recently drawn much attention due to the widespread existence of multi-modal data, and it generally involves two challenges: how to model the correlations and how to utilize the class...
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Chapter and Conference Paper
Deep Learning with PCANet for Human Age Estimation
Human age, as an important personal feature, has attracted great attention. Age estimation has also been considered as complex problem, how to get distinct age trait is important. In this paper, we investigate...
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Chapter and Conference Paper
A Nonparametric Hierarchical Bayesian Model and Its Application on Multimodal Person Identity Verification
In this paper, we propose a hierarchical Dirichlet process (HDP) mixture model of inverted Dirichlet (ID) distributions. The proposed model is learned within a principled variational Bayesian framework that we...
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Chapter and Conference Paper
A Novel Image Segmentation Approach Based on Truncated Infinite Student’s t-mixture Model
Mixture models have been used as efficient techniques in the application of image segmentation. In order to segment images automatically without knowing the number of true image components, the framework of Di...
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Chapter and Conference Paper
Nonparametric Localized Feature Selection via a Dirichlet Process Mixture of Generalized Dirichlet Distributions
In this paper, we propose a novel Bayesian nonparametric statistical approach of simultaneous clustering and localized feature selection for unsupervised learning. The proposed model is based on a mixture of D...
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Chapter and Conference Paper
A Variational Statistical Framework for Object Detection
In this paper, we propose a variational framework of finite Dirichlet mixture models and apply it to the challenging problem of object detection in static images. In our approach, the detection technique is ba...
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Chapter and Conference Paper
Generating Video Textures by PPCA and Gaussian Process Dynamical Model
Video texture is a new type of medium which can provide a continuous, infinitely varying stream of video images from a recorded video clip. It can be synthesized by rearranging the order of frames based on the...
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Chapter and Conference Paper
Online Video Textures Generation
In this paper, we propose two different online approaches for synthesizing video textures. The first approach is through incremental Isomap and Autoregressive (AR) process. It can generate good quality results...
