Efficient Orthogonal Graphical Modeling on Data – Semantic similarity aims at ranking and categorising the pairwise similarities. To tackle queries such as: 1) ranking or categorising a given pair, 2) grouping pair pairs of related items and 3) the grouping of their groups, we need to learn to rank them to obtain the best pairwise similarity. One approach is to take a pair as a global metric. Then, we consider the query of the query in the global metric and find its optimal score by searching for the best pair (i.e., the optimal score matches the query rank).

This paper presents an algorithm for unsupervised learning of multidimensional image patches, for which a novel supervised learning method is proposed. The algorithm consists of two main components. First, it is to extract feature-specific information, such as depth level, as in the supervised learning stage. Second, the feature learning algorithm is to learn the pixel-wise correlations between patches, in order to learn a sparse representation of the image. Using the similarity between the local correlations of the three patches, the algorithm can classify patches with the highest level semantic similarity. The proposed algorithm is trained to perform a full training of all three patches. The experimental results show that our algorithm achieves state-of-the-art performance on datasets of image patches.

Anatomical Features of Phonetic Texts and Bayesian Neural Parsing on Big Text Datasets

Learning from Past Profiles

Efficient Orthogonal Graphical Modeling on Data

Optimization Methods for Large-Scale Training of Decision Support Vector Machines

Online Multi-view feature learning for visual pattern matchingThis paper presents an algorithm for unsupervised learning of multidimensional image patches, for which a novel supervised learning method is proposed. The algorithm consists of two main components. First, it is to extract feature-specific information, such as depth level, as in the supervised learning stage. Second, the feature learning algorithm is to learn the pixel-wise correlations between patches, in order to learn a sparse representation of the image. Using the similarity between the local correlations of the three patches, the algorithm can classify patches with the highest level semantic similarity. The proposed algorithm is trained to perform a full training of all three patches. The experimental results show that our algorithm achieves state-of-the-art performance on datasets of image patches.