Our Research Topics

Topic Analysis of Minutes of the National Diet of Japan

• We provide this visualization only in Japanese.

Extraction of Topic Evolutions from References in Scientific Articles and Its GPU Acceleration

• This work tried to extract latent topic transitions from linked documents as a single transition matrix between latent topics.
• The paper was accepted as a short paper for CIKM 2012.
  • The original long version of the paper is available at this scribd page.
• This is a joint work with Prof. Atsuhiro Takasu in NII.

Clustering of the MNIST training images of the digit "6"

• This is a visualization of clustering [Masada+ ICONIP2013].

• The result given above is obtained for the MNIST dataset.

• The number of clusters is controlled by Chinese restaurant process.

• This result corresponds to "one" among the "three"-ways of our clustering in [Masada+ ICONIP2013].

Clustering of pixel columns and pixel rows

of hand-written digit images

• This is a visualization of clustering [Masada+ ICONIP2013].
• The result given above is obtained for the MNIST dataset.

• Black pixels are the pixels detected as irrelevant by our method.
• We can use clustering results for classifying test images.

Extracting visual topics from tens of thousands of images with GPU

• We implement CVB for LDA with CUDA [Masada+ IEA/AIE2009] and run an inference over 32,000 images.
  • This is a subset of Tiny Images dataset^[1] .
• Here we give the topic extraction results only for tens of images. The full set of results can be browsed at this Web page.
• Gray scale pixel values show topic probabilities at each pixel in each image.

Segmenting citation data with latent permutations

• We propose a completely new citation segmentation method based on a proposal by Chen et al.^[2]
  • Generalized Mallows model is used effectively for extending LDA to realize topic sequence mining.
• We proposed an unsupervised method [Masada+ WISS2010] and its semi-supervised version [Masada+ ICADL2011].
  • The above figure presents a segmentation example obtained by our semi-supervised segmentation.
• This is a joint work with Prof. Atsuhiro Takasu in NII.

Extracting topical trends

• We propose two methods, i.e., LYNDA and BToT, for extracting topical trends from a document set.
• The three figures above give the results obtained by LDA, LYNDA[Masada+ CIKM2009], and BToT[Masada+ ISNN2010].
  • Each colored region corresponds to a different topic.
  • Vertical axis represents document dates ranging from Jan. 1, 2002 to Dec. 31, 2005.
  • Horizontal axis represents topic popularity at each date.
  • The analyzed data is a set of Yomiuri newspaper articles.
  • You can try an interactive presentation of the results at the following places: LDA, LYNDA, and BToT.
• This is a joint research with Prof. Atsuhiro Takasu in NII.

Extracting per-topic temporal transitions of popular words from parallel corpora

• We propose a new topic model [Masada+ PAKDD2011] for extracting temporal transitions of word probabilities for each topic.
• Our model is extended for parallel corpus analysis and is applied to Chinese-English abstracts of computer science papers.
  • The years of the abstracts range from 2000 to 2009.
  • We only show five among tens of the extracted topics.
  • Each topic is represented by the top three Chinese and English words of large probability in each year.
  • No Chinese-English dictionaries are used.
  • This is a joint research with Prof. Atsuhiro Takasu in NII. The dataset was collected and cleaned up by Haipeng Zhang.