Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Graph convolutional and random Fourier feature mapping for hyperspectral image clustering.

Jointly stochastic fully symmetric interpolatory rules and local approximation for scalable Gaussian process regression.

Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder.

Joint U-Nets with hierarchical graph structure and sparse Transformer for hyperspectral image classification.

Geometric Parameter Effects on Bandgap Characteristics of Periodic Pile Barriers in Passive Vibration Isolation.

Leveraging self-paced learning and deep sparse embedding for image clustering.

Similarity Learning for Land Use Scene-Level Change Detection.

Joint contrastive self-supervised learning and weak-orthogonal product quantization for fast image retrieval.

Join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering.

Clustering by sparse orthogonal NMF and interpretable neural network.

One-step subspace clustering based on adaptive graph regularization and correntropy induced metric.

Large-Scale Gaussian Process Regression Based on Random Fourier Features and Local Approximation with Tsallis Entropy.

Image classification based on weighted nonconvex low-rank and discriminant least squares regression.

Noise-tolerant clustering via joint doubly stochastic matrix regularization and dual sparse coding.

Noise-aware clustering based on maximum correntropy criterion and adaptive graph regularization.

One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization.

Exemplar-based large-scale low-rank matrix decomposition for collaborative prediction.

Entropy Regularized Unsupervised Clustering Based on Maximum Correntropy Criterion and Adaptive Neighbors.

Robust clustering with sparse corruption via ℓ2, 1, ℓ1 norm constraint and Laplacian regularization.

Consensus kernel subspace clustering based on coefficient discriminant information.

Top-<i>K</i> interesting preference rules mining based on MaxClique.

Adaptive Graph Regularized Low-Rank Matrix Factorization With Noise and Outliers for Clustering.

Multiple Kernel Subspace Clustering Based on Consensus Hilbert Space and Second-Order Neighbors.

Efficient preference clustering via random fourier features.

CP-Nets Structure Learning Based on mRMCR Principle.

基于Nystrm采样和凸NMF的偏好聚类 (Preference Clustering Based on Nystrm Sampling and Convex-NMF).

CP-nets学习的复杂度 (Complexity of CP-nets Learning).

Region compatibility based stability assessment for decision trees.

Learning CP-Nets Structure From Preference Data Streams.

An effective and efficient hierarchical <i>K</i>-means clustering algorithm.

Learning Corpus-Invariant Discriminant Feature Representations for Speech Emotion Recognition.

Operators of preference composition for CP-nets.

Cross-corpus speech emotion recognition based on transfer non-negative matrix factorization.

The Treewidth of Induced Graphs of Conditional Preference Networks Is Small.

Transfer Semi-Supervised Non-Negative Matrix Factorization for Speech Emotion Recognition.

CP-nets的可满足性序列求解算法研究 (Research on Algorithm of Satisfiability Ranking Generation for CP-nets).

Expressive efficiency of two kinds of specific CP-nets.