We analyze under which conditions the missing label problem associated to a reduction chain s ⊂ s of (simple) Lie algebras can be completely solved by means of an Inönü-Wigner contraction g naturally related to the embedding. This provides a new interpretation of the missing label operators in terms of the Casimir operators of the contracted algebra, and shows that the available labeling operat...

Gene function has been a subject of interest but it is far from fully understood. It is known that some genes have certain functions but it is not clear whether those are all the functions they have. It is a recent trend to use different means to predict gene functions; one of them is to use computational methods on large data sets. Different types of information are used in computational metho...

(Received 00 Month 200x; in final form 00 Month 200x) Recognizing actions from a monocular video is a very hot topic in computer vision recently. In this paper, we propose a new representation of actions, the co-occurrence matrices de-scriptor, on the intrinsic shape manifold learned by graph embedding. The co-occurrence matrices descriptor captures more temporal information than the bag of wor...

Multi-label classification aims to recognize multiple objects or attributes from images. The key solving this issue relies on effectively characterizing the inter-label correlations dependencies, which bring prevailing graph neural network. However, current methods often use co-occurrence probability of labels based training set as adjacency matrix model correlation, is greatly limited by datas...

Dimensionality reduction is a topic of recent interest. In this paper, we present the classification constrained dimensionality reduction (CCDR) algorithm to account for label information. The algorithm can account for multiple classes as well as the semi-supervised setting. We present an out-of-sample expressions for both labeled and unlabeled data. For unlabeled data, we introduce a method of...

As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data. To address this problem, ZSL is usually carried out in the following two aspects: 1) capturing the domain distribution connections between seen classes data and unseen classes data; and 2) modeling the semantic int...

Numerous embedding models have been recently explored to incorporate semantic knowledge into visual recognition. Existing methods typically focus on minimizing the distance between the corresponding images and texts in the embedding space but do not explicitly optimize the underlying structure. Our key observation is that modeling the pairwise image-image relationship improves the discriminatio...

When a digital forensics investigator suspects that steganography has been used to hide data in an image, he must not only determine that the image contains embedded information but also identify the method used for embedding. The determination of the embedding method – or stego fingerprint – is critical to extracting the hidden information. This paper focuses on identifying stego fingerprints ...

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, ...