Visual data such as videos are often sampled from complex manifold. We propose leveraging the manifold structure to constrain the deep action feature learning, thereby minimizing the intra-class variations in the feature space and alleviating the over-fitting problem. Considering that manifold can be transferred, layer by layer, from the data domain to the deep features, the manifold priori is ...

We present a novel method for multitask learning (MTL) based on manifold regularization: assume that all task parameters lie on a manifold. This is the generalization of a common assumption made in the existing literature: task parameters share a common linear subspace. One proposed method uses the projection distance from the manifold to regularize the task parameters. The manifold structure a...

OF THE DISSERTATION Modeling Human Motion Using Manifold Learning and Factorized Generative Models by Chan-Su Lee Dissertation Director: Ahmed Elgammal Modeling the dynamic shape and appearance of articulated moving objects is essential for human motion analysis, tracking, synthesis, and other computer vision problems. Modeling the shape and appearance of human motion is challenging due to the ...

Existing manifold learning methods are not appropriate for image retrieval task, because most of them are unable to process query image and they have much additional computational cost especially for large scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned off-line by unsupervised strategy, to explore the intrinsic manifolds by i...

Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a lower-dimensional smooth manifold. There are a rich variety of manifold learning methods available, which allow mapping of data points to the manifold. However, there is a clear lack of probabilistic methods that allow...

We propose an appearance manifold with view-dependent covariance matrix for face recognition from video sequences in two learning frameworks: the supervised-learning and the incremental unsupervisedlearning. The advantages of this method are, first, the appearance manifold with view-dependent covariance matrix model is robust to pose changes and is also noise invariant, since the embedded covar...

Dimension reduction is considered as a necessary technique in Electronic Healthcare Records (EHR) data processing. However, no existing work addresses both of the two points: 1) generating low-dimensional representations for each patient visit; and 2) taking advantage well-organized medical concept structure domain knowledge. Hence, we propose new framework to generate records by combining conc...

In many machine learning problems, high-dimensional datasets often lie on or near manifolds of locally low-rank. This knowledge can be exploited to avoid the “curse of dimensionality” when learning a classifier. Explicit manifold learning formulations such as lle are rarely used for this purpose, and instead classifiers may make use of methods such as local co-ordinate coding or auto-encoders t...

Manifold learning is a powerful tool for reducing the dimensionality of a dataset by finding a low-dimensional embedding that retains important geometric and topological features. In many applications it is desirable to add new samples to a previously learnt embedding, this process of adding new samples is known as the out-ofsample extension problem. Since many manifold learning algorithms do n...