Distributed Binary Subspace Learning on large-scale cross media data

Distributed Binary Subspace Learning on large-scale cross media data   

                            Distributed Binary Subspace Learning on large-scale cross media data.Due to the ubiquitous existence of largescale data in today’s real-world applications including learningon cross media data, we propose a semi-supervised learning method named Multiple Binary SubspaceRegression (MBSR) for cross media data classification. In order to mine the common features among the data with multiple modalities, we project the original crossmedia data into the same low-rank representation simultaneously by mapping to the corresponding subspaces for dimension reduction.

Hadoop-Projects

Hadoop-Projects

All the subspaces are set to be binary, which only involve the addition operations and omit the multiplication operations in the subsequent computation owing to the good property of the binary values. The dimension reduction to a binary subspace and the classification on this subspace are also optimized simultaneously leading to a semi-supervised model. For dealing with largescale data, our learningmethod is easily implemented to run in a MapReduce-based Hadoop system. Empirical studies demonstrate its competitive performance on convergence, efficiency, and scalability in comparison with the state-of-the-art literature.

Similar IEEE  Project Titles

Save


Work Progress

PHD - 24

M.TECH - 125

B.TECH -95

BIG DATA -110.

HADOOP -90.

ON-GOING Hadoop Projects

HADOOP MAP -90.

HADOOP YARN -27.

HADOOP HEBROS - 25.

HADOOP ZOOKEEPER -18.

Achievements – Hadoop Solutions

Hadoop-Projects-Achievement-Awards

Twitter Feed

Customer Review

Hadoop Solutions 5 Star Rating: Recommended 4.9 - 5 based on 1000+ ratings. 1000+ user reviews.