Partial label learning refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because
it requires the distance matrix to compute the density and-distance for each object, which …

Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power
consumption and contributing to several challenges encountered when transiting to an …

The metaverse is an Artificial Intelligence (AI)-generated virtual world, in which people can
game, work, learn, and socialize. The realization of metaverse not only requires a large …

Granular-ball computing (GBC) is an efficient, robust, and scalable learning method for
granular computing. The granular ball (GB) generation method is based on GB computing …

Label distribution learning is a widely studied supervised learning diagram that can handle
the problem of label ambiguity. The increasing size of datasets is accompanied by the …

In order to solve the problem that the traditional spectral clustering algorithm is time-
consuming and resource consuming when applied to large-scale data, resulting in poor …

Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …

The decision tree algorithm has been widely used in data mining and machine learning due
to its high accuracy, low computational cost and high interpretability. However, when dealing …

Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting
a series of informative and relevant attributes from data sets. The increase in dimensionality …