-
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Cross-view classification that means to classify samples from heterogene...
read it
-
Sparse Linear Discriminant Analysis for Multi-view Structured Data
Classification methods that leverage the strengths of data from multiple...
read it
-
Incremental Fast Subclass Discriminant Analysis
This paper proposes an incremental solution to Fast Subclass Discriminan...
read it
-
Constrained Mutual Convex Cone Method for Image Set Based Recognition
In this paper, we propose a method for image-set classification based on...
read it
-
Null Space Analysis for Class-Specific Discriminant Learning
In this paper, we carry out null space analysis for Class-Specific Discr...
read it
-
Multi-view Deep Features for Robust Facial Kinship Verification
Automatic kinship verification from facial images is an emerging researc...
read it
-
A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI
With the growth of location-based services, indoor localization is attra...
read it
Speed-up and multi-view extensions to Subclass Discriminant Analysis
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution for it can be obtained in a similar to the single-view manner. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.
READ FULL TEXT
Comments
There are no comments yet.