Today abundant information is available due to the advent of Internet, which is usually stored with sole purpose of current needs alone. Such data thus rest in unclassified in dump repository. Instead if it would be stored in a classified repository then navigation could be done easily, or classified at the later stage reaching it could become easier and thus could helpful in decision making. I...

MOTIVATION Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult f...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

A semi-supervised clustering algorithm is proposed that combines the benefits of supervised and unsupervised learning methods. Data are segmented/clustered using an unsupervised learning technique that is biased toward producing segments or clusters as pure as possible in terms of class distribution. These clusters can then be used to predict the class of future points. For example in database ...

Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic...

In this paper, we present a novel interactive image retrieval technique using semi-supervised learning. Recently, Guan and Qiu [8, 9] have shown that by constructing a Bayesian Network where the nodes represent the (continuous) class membership scores and arcs represent the dependence relations of the data points, the (semi-supervised) classification problem can be formulated as a quadratic opt...

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we int...

Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic...