With the increasing amount of information on the internet, recommendation system (RS) has
been utilized in a variety of fields as an efficient tool to overcome information overload. In …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS)
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

In this work, we investigate the problem of learning knowledge from the massive community-
contributed images with rich weakly-supervised context information, which can benefit …

Hashing has been widely investigated for large-scale image retrieval due to its search
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …

IID 2 hypothesis between training and testing data is the basis of numerous image
classification methods. Such property can hardly be guaranteed in practice where the Non …

Alzheimer's disease (AD) is one of the most difficult to cure diseases. Alzheimer's disease
seriously affects the normal lives of the elderly and their families. The mild cognitive …

In this paper we propose a model retraining method for learning more efficient convolutional
representations for Content Based Image Retrieval. We employ a deep CNN model to obtain …

Visual grounding (VG) aims to locate a specific target in an image based on a given
language query. The discriminative information from context is important for distinguishing …