Kernel Methods are algorithms that projects input data by a nonlinear mapping in a new space (Feature Space). In this thesis we have investigated Kernel Methods for Unsupervised learning, namely Kernel Methods that do not require targeted data. Two classical unsupervised learning problems using Kernel Methods have been tackled. The former is the Data Dimensionality Estimation, the latter is the...

In this paper, we propose a multimodal unsupervised video learning algorithm designed to incorporate information from any number of modalities present in the data. We cooperatively train network corresponding each modality: at stage training, one these networks is selected be trained using output other networks. To verify our algorithm, model RGB, optical flow, and audio. then evaluate effectiv...

The desire to reduce the dependence on curated, labeled datasets and leverage vast quantities of unlabeled data has triggered renewed interest in unsupervised (or self-supervised) learning algorithms. Despite improved performance due approaches such as identification disentangled latent representations, contrastive clustering optimizations, machine still falls short its hypothesized potential a...

Traditionally, adaptive learning systems are classified into two distinct paradigms---supervised and unsupervised learning. Although a lot of results have been published in these two learning paradigms, the relations between them have been seldom investigated. In this paper we focus on the relationship between the two kinds of learning and show that in a linear network the supervised learning w...

1. Introduction From a traditional point of view, knowledge exploration can be categorized into supervised learning and unsupervised learning (Jordan and Jacobs 1994). In the last decade, there have been research activities on supervised learning approaches and techniques, whereby class information is available before any knowledge exploration takes place. The most utilized approach is to achie...

There are mainly two kinds of methods for document-level sentiment classification, unsupervised learning and supervised learning. When ensemble learning is introduced, existing methods only combine unsupervised learning algorithms or supervised learning algorithms. To overcome each other’s flaws, a novel sentiment classification method based on behavior-knowledge space is proposed, in which two...

The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning mod...

Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typica...

The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training points (x alone). For the labeled points, supervised learning techniques apply, but they cannot take advantage of the unlabeled points. On the other hand, unsupervised techniques can model the unlabeled data distribution, but do not exploit the labels. Thus, this task falls between traditional s...

Classification task involves inducing a predictive model using a set of labeled samples. The more the labeled samples are, the better the model is. When one has only a few samples, the obtained model tends to offer poor result. Even when labeled samples are difficult to get, a lot of unlabeled samples are generally available on which unsupervised learning can be used. In this paper, a way to co...