Search Results

(1) In this work, a set of deep autoencoders with different numbers of layers and layer sizes were trained using a set of open access hand tracking datasets. Examining the results allowed to determine optimal number of layers for deep autoencoders and ...

Vibrational response identification of high-rise building structures under excitation of varying ambient conditions is of great significance for structural vibration control and health monitoring. Traditional strategies allocating time-frequency domain ...

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant ...

Depression detection has become an arduous task in social media due to its complicated association with mental disorders. This work focuses on extracting the depressive features in the social network from the unstructured and structured data through the ...

Multi-view clustering (MVC) aims at exploiting the consistent features within different views to divide samples into different clusters. Existing subspace-based MVC algorithms usually assume linear subspace structures and two-stage similarity matrix ...

The stock market prediction problems have received increased attention from researchers due to the high stakes involved and the need for better prediction accuracy. We have developed an architecture by combining a deep autoencoder and long short-term ...

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in ...

Detecting social bots and identifying social botnet communities are extremely important in online social networks (OSNs). In this paper, we first construct a weighted signed Twitter network graph based on the behavioral similarity and trust values ...

Graph embedding, also known as network embedding and network representation learning, is a useful technique which helps researchers analyze information networks through embedding a network into a low-dimensional space. However, existing graph embedding ...

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of ...

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from ...

Due to its storage and query efficiency, hashing has been widely applied to approximate nearest neighbor search from large-scale datasets. While there is increasing interest in cross-modal hashing which facilitates cross-media retrieval by embedding ...

For the challenging pole balancing task we propose a system which uses raw visual input data for reinforcement learning to evolve a control strategy. Therefore we use a neural network --- a deep autoencoder --- to encode the camera images and thus the ...