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Knowledge Discovery from Citation Networks

Knowledge discovery from citation networks (i.e., textual data with links such as scientific articles, legal documents, webpages, and emails) provides insight into vast areas since huge repositories are made available by internet and digital databases. Digital libraries allow for the organization of an expansive amount of publications in a structured...
Published: 1/28/2020   |   Inventor(s): Zhongfei Zhang, Zhen Guo
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Campus > Binghamton University

Semi-supervised Learning Based on Semiparametric Regularization

Labeled data are often expensive to obtain since they require the efforts of experienced experts. Meanwhile, the unlabeled data are relatively easy to collect. Semiparametric regularization semi-supervised learning attempts to use the unlabeled data to improve the performance. Experimental comparisons demonstrate that our approach outperforms the state-of-the-art...
Published: 1/27/2020   |   Inventor(s): Zhongfei Zhang, Guo Zhen
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Technology Classifications > Information Technology

Enhanced Max Margin Learning on Multimodal Data Mining in a Multimedia Database

The problem of multimodal data mining in a multimedia database can be addressed as a structured prediction problem where we learn the mapping from an input to the structured and interdependent output variables. Built upon the existing literature on the max margin-based learning, we develop a new max margin learning approach called Enhanced Max Margin...
Published: 1/27/2020   |   Inventor(s): Zhen Guo, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Campus > Binghamton University

Mixed Membership Relational Clustering

Most clustering approaches in the literature focus on "flat" data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other, such as documents and words in a text corpus, Web pages, search queries...
Published: 1/27/2020   |   Inventor(s): Bo Long, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Campus > Binghamton University

Spectral Relational Clustering, Multi-type Relational Data, Collective Factorization on Related Matrices

Most clustering approaches in the literature focus on "flat" data in which each data object is represented as a fixed-length feature vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other, such as Web pages, search queries and Web users in a Web search system,...
Published: 1/27/2020   |   Inventor(s): Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Campus > Binghamton University

Soft Correspondence Ensemble Clustering (SCEC)

The developed framework is based on soft correspondence to directly address the correspondence problem of clustering ensembles. By the concept of soft correspondence, a cluster from one clustering corresponds to each cluster from another clustering with different weight. Under this framework, we define a correspondence matrix as an optimal solution to...
Published: 1/27/2020   |   Inventor(s): Bo Long, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers

Method and Apparatus for Image Annotation and Multimodal Image Retrieval

The technology is based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer, which constitutes the semantic concepts to be discovered to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework such that...
Published: 1/27/2020   |   Inventor(s): Ruofei Zhang, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers

Semisupervised Autoencoder for Sentiment Analysis

Autoencoders have attracted a lot of attention as a building block of Deep Learning and modeling textual data. They act as the feature learning methods by reconstructing inputs with respect to a given loss function. In a neural network implementation of autoencoders, the hidden layer is taken as the learned feature. While it is often trivial to obtain...
Published: 1/17/2020   |   Inventor(s): Shuangfei Zhai, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Artificial Intelligence, Campus > Binghamton University

Pattern Change Discovery Between High Dimensional Datasets

This technology provides a powerful solution to the general problem of pattern change discovery between high-dimensional data sets. Current technologies either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. In this technology, the notion of the principal angles between the subspaces is introduced...
Published: 1/12/2020   |   Inventor(s): Yi Xu, Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Technology Classifications > Information Technology, Campus > Binghamton University

Fast, Automatic Detection of Independent Motion from Compresses Surveillance Video

An algorithm that automates qualitative surveillance video detection of independently moving objects in two modes: (1) real-time (in combination with sensors) and (2) very fast scanning (faster than real-time play speed) of surveillance video database and retrieval of shots containing detected movement for further review by image analysts. The approach...
Published: 1/12/2020   |   Inventor(s): Zhongfei Zhang
Keywords(s): #SUNYresearch, Technology
Category(s): Technology Classifications > Computers, Technology Classifications > Artificial Intelligence, Campus > Binghamton University

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