SUMAA is a hybrid algorithm based on letter successor varieties for an en­ tirely unsupervised morphological analysis. Using language pattern and structural recognition it works well on both isolated and agglutinative lan­ guages. This paper gives a detailed analysis of how we developed SUMAA. F-Measures (MorphoChal­ lenge, 2005) achieved by SUMAA for the English, Finnish and Turkish datasets w...

We propose a formulation of a new problem, which we call change analysis, and a novel method for solving the problem. In contrast to the existing methods of change (or outlier) detection, the goal of change analysis goes beyond detecting whether or not any changes exist. Its ultimate goal is to find the explanation of the changes. While change analysis falls in the category of unsupervised lear...

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simultaneously learns feature representations and cluster assignments using deep neural network...

This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method ocality preserving projection (LP...

In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusi...

This paper describes the specifications and results of SSA-UO, unsupervised system, presented in SemEval 2013 for Sentiment Analysis in Twitter (Task 2) (Wilson et al., 2013). The proposal system includes three phases: data preprocessing, contextual word polarity detection and message classification. The preprocessing phase comprises treatment of emoticon, slang terms, lemmatization and POS-tag...

In this paper, a novel method is presented for unsupervised image segmentation based on local homogeneity analysis. First, a criterion for homogeneity of a certain pattern is proposed. Applying the criterion to local windows in the original image results in the “H-image”. The high and low values of the H-image correspond to possible region boundaries and region interiors respectively. Then, a r...

This paper describes a system for unsupervised morpheme analysis and the results it obtained at Morpho Challenge 2007. The system takes a plain list of words as input and returns a list of labelled morphemic segments for each word. Morphemic segments are obtained by an unsupervised learning process which can directly be applied to different natural languages. Results obtained at competition 1 (...

In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this overwhelming amount of data stimulates a growing need to develop fast and accurate algorithms to discover useful information hidden in the data. This need is even more acute for unsupervised data, which lacks information about the c...

2 Analysis of a pig fetus wholy body cross section 1 2.1 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Peak picking and alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Visualizin...