In our system, the MAE predictive values of Valence and Arousal were 0. 811 and 0. 996, respectively, for the sentiment dimension prediction of words in Chinese.
In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).
The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation.
The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.
The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure.
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.
In this problem, each document is composed two different modals of data, i. e., an image and a text.
This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set.