Accession Number:

ADA621842

Title:

Unsupervised Learning from Multiple Information Sources Based on Non-negative Matrix Factorization (NMF)

Descriptive Note:

Final rept. 19 Aug 2010-18 Aug 2014

Corporate Author:

FLORIDA INTERNATIONAL UNIV MIAMI

Personal Author(s):

• Li, Tao

Report Date:

2015-01-20

Pagination or Media Count:

15.0

Abstract:

In many real-world applications, the data are naturally multi-modal, in the sense that they are represented by multiple sets of features. In general, with the availability of multiple information sources, it is a challenging problem to conduct integrated exploratory analysis with the aim of extracting more information than what is possible from only a single source.

Descriptors:

• *COMPUTER VISION
• *DATA FUSION
• *FEATURE EXTRACTION
• *IMAGE PROCESSING
• *MULTISENSORS
• ARTIFICIAL INTELLIGENCE
• CLUSTERING
• COLLABORATIVE TECHNIQUES
• DATA MINING
• GRAPHS
• INFORMATION EXCHANGE
• INTEGRATED SYSTEMS
• MATRICES(MATHEMATICS)
• NETWORK ARCHITECTURE
• PATTERN RECOGNITION
• PROBABILITY
• SEMANTICS
• TARGET CLASSIFICATION
• TEXT PROCESSING
• VISUAL SURVEILLANCE

Subject Categories:

• Information Science
• Statistics and Probability
• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE