Search Results for author:
Found 27 papers, 6 papers with code
Diving Deep into Regions: Exploiting Regional Information Transformer for Single Image Deraining
Our RTB is used for attention selection of rain-affected and unaffected regions and local modeling of mixed scales.
Spectral Clustering for Discrete Distributions
Discrete distribution clustering (D2C) was often solved by Wasserstein barycenter methods.
Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning
Secondly, we develop a series of affinity learning methods that equip the selfexpressive framework with ℓp-norm to construct an intrinsic affinity matrix with an adaptive extension.
Self-Discriminative Modeling for Anomalous Graph Detection
Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection.
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
Restricted Generative Projection for One-Class Classification and Anomaly Detection
The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution.
Deep Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection
Graph-level anomaly detection aims to identify anomalous graphs from a collection of graphs in an unsupervised manner.
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark
Specifically, we present a decoupled interaction module (DIM) that aims for sufficient dual-view information interaction.
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization
Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data.
Unsupervised Deep Discriminant Analysis Based Clustering
This work presents an unsupervised deep discriminant analysis for clustering.
Perturbation Learning Based Anomaly Detection
This paper presents a simple yet effective method for anomaly detection.
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization
To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated.
Efficient Deep Embedded Subspace Clustering
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice
We provide theoretical analysis for NE-AECF to investigate the generalization ability of autoencoder and deep learning in collaborative filtering.
Multi-Mode Deep Matrix and Tensor Factorization
This paper presents a framework of multi-mode deep matrix and tensor factorizations to explore and exploit the full nonlinearity of the data in matrices and tensors.
A Simple Approach to Automated Spectral Clustering
First, we propose to find the most reliable affinity matrix via grid search or Bayesian optimization among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper.
Large-Scale Subspace Clustering via k-Factorization
This paper presents a method called k-Factorization Subspace Clustering (k-FSC) for large-scale subspace clustering.
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.
$k$FW: A Frank-Wolfe style algorithm with stronger subproblem oracles
This paper proposes a new variant of Frank-Wolfe (FW), called $k$FW.
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components.
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
RNLMF constructs a dictionary for the data space by factoring a kernelized feature space; a noisy matrix can then be decomposed as the sum of a sparse noise matrix and a clean data matrix that lies in a low dimensional nonlinear manifold.
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition
Overall, our work unified graph embedding features to promotes systematic research on human action recognition.
Online high rank matrix completion
Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure.
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension.
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank.
Exactly Robust Kernel Principal Component Analysis
RKPCA can be applied to many problems such as noise removal and subspace clustering and is still the only unsupervised nonlinear method robust to sparse noises.
