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Found 18 papers, 0 papers with code
Cross-model convolutional neural network for multiple modality data representation
A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities.
A novel transfer learning method based on common space mapping and weighted domain matching
We map the data of two domains to one single common space, and learn a classifier in this common space.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure.
Supervised cross-modal factor analysis for multiple modal data classification
In this problem, each document is composed two different modals of data, i. e., an image and a text.
Sparse Coding with Earth Mover's Distance for Multi-Instance Histogram Representation
It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error.
Regularized maximum correntropy machine
To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework.
Multi-view learning for multivariate performance measures optimization
We propose to learn a linear discriminant functions for each view, and combine them to construct a overall multivariate mapping function for mult-view data.
Learning manifold to regularize nonnegative matrix factorization
Recently, manifold regularized NMF used a nearest neighbor graph to regulate the learning of factorization parameter matrices and has shown its advantage over traditional NMF methods for data representation problems.
Maximum mutual information regularized classification
In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label.
Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively.
When coding meets ranking: A joint framework based on local learning
In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm.
Domain Transfer Structured Output Learning
The problem is to learn a predictor for the target domain to predict the structured outputs from the input.
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets.
Max-Min Distance Nonnegative Matrix Factorization
It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basic matrix and a nonnegative coefficient matrix, and the coefficient matrix is used as the new representation.
Cross-Domain Sparse Coding
Sparse coding has shown its power as an effective data representation method.
Semi-Supervised Sparse Coding
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations.
Multiple graph regularized protein domain ranking
However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.
