Home Textile Pattern Emotion Labeling Using Deep Multi-View Feature Learning

EEG Based Emotion Identification Using Unsupervised Deep Feature Learning

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEGbased emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract highlevel features from...

On multi-view feature learning

Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that h...

Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Networ...

Active Labeling in Deep Learning and Its Application to Emotion

Multi-view Acoustic Feature Learning Using Articulatory Measurements

We consider the problem of learning a linear transformation of acoustic feature vectors for phonetic frame classification, in a setting where articulatory measurements are available at training time. We use the acoustic and articulatory data together in a multi-view learning approach, in particular using canonical correlation analysis to learn linear transformations of the acoustic features tha...