Senya Kiyasu

Comparing LDA with pLSI as a dimensionality reduction method in document clustering

In this paper, we compare latent Dirichlet allocation (LDA) with probabilistic latent semantic indexing (pLSI) as a dimensionality reduction method and investigate their effectiveness in document clustering by using real-world document sets. For clustering of documents, we use a method based on multinomial mixture, which is known as an efficient framework for text mining. Clustering results are evaluated by F-measure, i.e., harmonic mean of precision and recall. We use Japanese and Korean Web articles for evaluation and regard the category assigned to each Web article as the ground truth for the evaluation of clustering results. Our experiment shows that the dimensionality reduction via LDA and pLSI results in document clusters of almost the same quality as those obtained by using original feature vectors. Therefore, we can reduce the vector dimension without degrading cluster quality. Further, both LDA and pLSI are more effective than random projection, the baseline method in our experiment. However, our experiment provides no meaningful difference between LDA and pLSI. This result suggests that LDA does not replace pLSI at least for dimensionality reduction in document clustering.

Adaptive Subpixel Estimation of Land Cover in a Remotely Sensed Multispectral Image

Land surface corresponding to a pixel of remotely sensed image does not necessarily consist of only one category of objects. Several techniques of subpixel analysis have been developed which estimate the proportion of components of land cover in a pixel. However, when the available training data do not correctly represent the spectral characteristics of the categories in the pixel, large errors may appear in the results of estimation. The method of unsupervised estimation of component spectra has been presented to solve this problem. In this paper we present a method which apply the unsupervised analysis technique to subpixel estimation of land cover in an image in which spectral characteristics change with the location of the objective area. After partitioning the image into blocks, the number of categories and their component spectra are estimated in each block. Then the proportion of category are estimated for each pixel using the component spectra derived in the block. We confirmed the validity of this method by numerical simulation

Unmixing three types of lung sounds by convex optimization

We present a convex optimization technique for unmixing lung sounds to improve computer-aided pulmonary auscultation. An auscultatory sound of a patient with pulmonary disorder may be composed of continuous and discontinuous adventitious sounds as well as breath. Our technique exploits sparse and low-rank properties of these sounds in the Fourier, wavelet, and time-frequency domains, which can be quantified as convex functions. The optimization algorithm is derived from the alternating direction method of multipliers (ADMM). This approach enables the lung sound unmixing without training data for learning diverse structures of lung sounds in time-frequency domains. We show some experimental examples and discuss further improvements.

Subpixel estimation of land cover in a remotely sensed image using spectral information of surrounding pixels

Several techniques of subpixel analysis for remotely sensed image have been developed which estimate the proportion of components of land cover in a pixel. However, when the available training data do not correctly represent the spectral characteristics of the categories in the pixel, large errors may appear in the results of estimation. In this paper, we propose a semi-supervised method of subpixel estimation of land cover for remotely sensed multispectral image. First we provide small size of initial training data and determine pure pixels in the image. In the next step, component spectra are adaptively estimated for each mixed pixel using the surrounding pure pixels. Then the proportions of components in the mixed pixels are estimated based on the determined component spectra. We confirmed the validity of this method by numerical simulation and applied it to a remotely sensed multispectral image.

Sparse representation-based extraction of pulmonary sound components from low-quality auscultation signals

Toward assistance of respiratory system diagnosis, sparse representation of auscultation signals is utilized to extract pulmonary sound components. This signal extraction is a challenging task because the pulmonary sounds such as vesicular sounds and crackles are overlapping each other in the time and frequency domains, and they are so faint that the quality of recorded signals is quite low in many cases. It is experimentally shown that the pulmonary sound components are successfully extracted from low-quality auscultation signals via the sparse representation. This extraction method is confirmed to be highly robust against random noise and digital quantization.

Browsing and Similarity Searching Method for Videos Based on Cluster Analysis

We developed a browsing and similarity searching method for videos based on cluster analysis. First, videos are segmented into shots by fuzzy clustering of graph spectral methods. Second, the videos are represented as a sequence of symbols by grouping together shots. According to this representation, the directed graph of videos is formed based on the relationship between these symbols. Initial and terminal shots are extracted from the di-rected graph using fuzzy cluster extraction. The shots can be browsed from the initial shots to the terminal ones sequentially. Selected shots are used as a query video on similarity searches. The performance of the proposed method was evaluated using a video dataset from NASA.

Browsing and similarity search of videos based on cluster extraction from graphs

This paper presents a browsing and similarity search method of videos based on cluster extraction from graphs. Videos are segmented into shots and represented as the sequence of symbols. The directed graph of videos is constructed from the relationship between those symbols. Initial and terminal shots are extracted from it, and they are displayed for users sequentially from the initial shots to the terminal ones. The shots selected by means of this browsing are used as a query video on similarity search. The performance of the proposed method is examined by using the video dataset of NASA.