An infant’s learning of visual representations is entirely unsupervised. While unsupervised neural network learning architectures had some successes in predicting the receptive field properties of early visual representations in the brain, it remains unclear how the formation of higher level representations can be understood. This paper argues that in order to understand the formation of these ...

With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffe...

Many Uralic languages have a rich morphological structure, but lack morphological analysis tools needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications. We study how to create a statistical model for morphological segmentation with a ...

This paper presents a new hybrid learning algorithm for unsupervised classification tasks. We combined Fuzzy c-means learning and the supervised version of Minimerror to develop a hybrid incremental strategy allowing unsupervised classifications. We applied this new approach to a real-world database in order to know if the information contained in unlabeled signals of a Geographic Information S...

Computer aid diagnosis for brain MRI image is widely used in hospitals. An important step in it is to recognize different regions within an MRI image according to medical experience. In this paper, we propose an unsupervised learning algorithm for automatic segmentation of MRI images. Different from previous methods, our method achieves the idea of visual segmentation, which simulates the think...

The Information Bottleneck is an information theoretic framework that finds concise representations for an ‘input’ random variable that are as relevant as possible for an ‘output’ random variable. This framework has been used successfully in various supervised and unsupervised applications. However, its learning theoretic properties and justification remained unclear as it differs from standard...

With the constantly changing and deceptive strategies that can be concealed in complex of financial statements, traditional means of financial analysis is unable to detect these accounting frauds in advance. In order to detect new accounting frauds and find out the true meaning of off-balance sheet arrangements, we propose an easy and feasible method using an unsupervised learning system. In un...

Conventional non-negative tensor factorization (NTF) methods assume there is only one tensor that needs to be decomposed to low-rank factors. However, in practice data are usually generated from different time periods or by different class labels, which are represented by a sequence of multiple tensors associated with different labels. This raises the problem that when one needs to analyze and ...

In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method for combining unsupervised and supervised rule-based training algorithms to create a highly accurate t...

This paper deals with an aspect of active object recognition for improving the classification and localization results by choosing optimal next views at an object. The knowledge of “good” next views at an object is learned automatically and unsupervised from the results of the used classifier. For that purpose methods of reinforcement learning are used in combination with numerical optimization...