Zhen T Guo

US Patent:

8234274, Jul 31, 2012

Filed:

Dec 1, 2009

Appl. No.:

12/629043

Inventors:

Zhen Guo - Elkins Park PA, US
Yun Chi - Santa Clara CA, US

Assignee:

NEC Laboratories America, Inc. - Princeton NJ

International Classification:

G06F 7/00
G06F 17/30

US Classification:

707726, 707729

Abstract:

Systems and methods are disclosed for extracting characteristics from a corpus of linked documents by deriving a content link model that explicitly captures direct and indirect relations represented by the links, and extracting document topics and the topic distributions for all the documents in the corpus using the content-link model.

US Patent:

8463053, Jun 11, 2013

Filed:

Aug 10, 2009

Appl. No.:

12/538845

Inventors:

Zhen Guo - Elkins Park PA, US
Zhongfei (Mark) Zhang - Vestal NY, US

Assignee:

The Research Foundation of State University of New York - Binghamton NY

International Classification:

G06K 9/62

US Classification:

382225

Abstract:

Multimodal data mining in a multimedia database is addressed as a structured prediction problem, wherein mapping from input to the structured and interdependent output variables is learned. A system and method for multimodal data mining is provided, comprising defining a multimodal data set comprising image information; representing image information of a data object as a set of feature vectors in a feature space; clustering in the feature space to group similar features; associating a non-image representation with a respective image data object based on the clustering; determining a joint feature representation of a respective data object as a mathematical weighted combination of a set of components of the joint feature representation; optimizing a weighting for a plurality of components of the mathematical weighted combination with respect to a prediction error between a predicted classification and a training classification; and employing the mathematical weighted combination for automatically classifying a new data object.

US Patent:

8527432, Sep 3, 2013

Filed:

Aug 10, 2009

Appl. No.:

12/538849

Inventors:

Zhen Guo - Elkins Park PA, US
Zhongfei (Mark) Zhang - Vernon CT, US

Assignee:

The Research Foundation of State University of New York - Binghamton NY

International Classification:

G06F 15/18
G06E 1/00
G06E 3/00
G06G 7/00

US Classification:

706 12, 706 14, 706 20

Abstract:

Semi-supervised learning plays an important role in machine learning and data mining. The semi-supervised learning problem is approached by developing semiparametric regularization, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. A semi-supervised learning approach is provided which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermore, the proposed approach which naturally extends to the out-of-sample data is an inductive learning method in nature.

US Patent:

8630975, Jan 14, 2014

Filed:

Dec 2, 2011

Appl. No.:

13/310098

Inventors:

Zhen Guo - Warrington PA, US
Mark Zhang - Vernon CT, US

Assignee:

The Research Foundation for The State University of New York - Binghamton NY

International Classification:

G06F 7/00
G06F 17/30

US Classification:

707608, 707724, 707726, 707738, 707749

Abstract:

In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. A Bernoulli Process Topic (BPT) model is provided which models the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach.