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Found 64 papers, 6 papers with code
Simple Multigraph Convolution Networks
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators.
Embedded Multi-label Feature Selection via Orthogonal Regression
Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data.
Multi-class Support Vector Machine with Maximizing Minimum Margin
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks.
A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization
To address the problems, we propose a novel N-Cut solver designed based on the famous coordinate descent method.
NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks
Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.
On the Global Solution of Soft k-Means
This paper presents an algorithm to solve the Soft k-Means problem globally.
Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).
A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges
In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes; (2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection
For feature engineering, feature selection seems to be an important research content in which is anticipated to select "excellent" features from candidate ones.
Adaptive neighborhood Metric learning
In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining.
New Tight Relaxations of Rank Minimization for Multi-Task Learning
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace.
Local $K$-means: An Efficient Optimization Algorithm And Its Generalization
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages.
Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection
However, real-world data contain a large number of noise samples and features, making the similarity matrix constructed by original data cannot be completely reliable.
Learning Feature Sparse Principal Subspace
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut
In particular, over 15x and 7x speed-up can be obtained with respect to $k$-means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
Agglomerative Neural Networks for Multi-view Clustering
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
Curriculum Audiovisual Learning
Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data.
Supervised feature selection with orthogonal regression and feature weighting
Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data.
Longitudinal Enrichment of Imaging Biomarker Representations for Improved Alzheimer's Disease Diagnosis
Longitudinal data is often available inconsistently across individuals resulting in ignoring of additionally available data.
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
In this paper, we propose a novel robust linear discriminant analysis method based on the L1, 2-norm ratio minimization.
Intrinsic Weight Learning Approach for Multi-view Clustering
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering.
Learning Feature Sparse Principal Components
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
Listen to the Image
different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.
Feature Learning Viewpoint of AdaBoost and a New Algorithm
We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.
Dense Multimodal Fusion for Hierarchically Joint Representation
Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities.
Deep LDA Hashing
The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does.
Low Rank Regularization: A Review
Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version.
Deep Multimodal Clustering for Unsupervised Audiovisual Learning
And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion.
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances
To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.
Regularized Singular Value Decomposition and Application to Recommender System
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA).
Ranking with Adaptive Neighbors
Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix.
Learning A Structured Optimal Bipartite Graph for Co-Clustering
In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.
A Self-Balanced Min-Cut Algorithm for Image Clustering
In the new method, a self-balanced min-cut model is proposed in which the Exclusive Lasso is implicitly introduced as a balance regularizer in order to produce balanced partition.
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction
It is interesting that the optimal regularization parameter is adaptive to the neighbors in low-dimensional space and has intuitive meaning.
Deep Binary Reconstruction for Cross-modal Hashing
Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion.
Local Shrunk Discriminant Analysis (LSDA)
Principal component analysis (PCA) is a traditional technique for unsupervised dimensionality reduction, which is often employed to seek a projection to best represent the data in a least-squares sense, but if the original data is nonlinear structure, the performance of PCA will quickly drop.
From Photo Streams to Evolving Situations
Photos are becoming spontaneous, objective, and universal sources of information.
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification
In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.
A Closed Form Solution to Multi-View Low-Rank Regression
Real life data often includes information from different channels.
Uncovering Locally Discriminative Structure for Feature Analysis
We propose a method that utilizes both the manifold structure of data and local discriminant information.
Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking
Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years.
Discriminatively Embedded K-Means for Multi-View Clustering
In real world applications, more and more data, for example, image/video data, are high dimensional and represented by multiple views which describe different perspectives of the data.
Non-Greedy L21-Norm Maximization for Principal Component Analysis
In this paper, we propose to maximize the L21-norm based robust PCA objective, which is theoretically connected to the minimization of reconstruction error.
Multi-View Subspace Clustering
In this paper, we propose a novel multi-view subspace clustering method.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
Unsupervised Feature Analysis with Class Margin Optimization
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation
In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.
Effective Discriminative Feature Selection with Non-trivial Solutions
In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization.
Exclusive Feature Learning on Arbitrary Structures via \ell_{1,2}-norm
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level.
Compound Rank-k Projections for Bilinear Analysis
In many real-world applications, data are represented by matrices or high-order tensors.
Improved Spectral Clustering via Embedded Label Propagation
Our algorithm is built upon two advancements of the state of the art:1) label propagation, which propagates a node\'s labels to neighboring nodes according to their proximity; and 2) manifold learning, which has been widely used in its capacity to leverage the manifold structure of data points.
A Convex Sparse PCA for Feature Analysis
In addition, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability.
Balanced k-Means and Min-Cut Clustering
Clustering is an effective technique in data mining to generate groups that are the matter of interest.
A Convex Formulation for Spectral Shrunk Clustering
Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance.
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine
We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction
Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions.
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach
In many graph-based machine learning and data mining approaches, the quality of the graph is critical.
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
The ℓ2, 1-norm based loss function is robust to outliers in data points and the ℓ2, 1-norm regularization selects features across all data points with joint sparsity.
