Multi-label Linear Discriminant Analysis
نویسندگان
چکیده
Multi-label problems arise frequently in image and video annotations, and many other related applications such as multi-topic text categorization, music classification, etc. Like other computer vision tasks, multi-label image and video annotations also suffer from the difficulty of high dimensionality because images often have a large number of features. Linear discriminant analysis (LDA) is a well-known method for dimensionality reduction. However, the classical Linear Discriminant Analysis (LDA) only works for single-label multi-class classifications and cannot be directly applied to multi-label multi-class classifications. It is desirable to naturally generalize the classical LDA to multi-label formulations. At the same time, multi-label data present a new opportunity to improve classification accuracy through label correlations, which are absent in single-label data. In this work, we propose a novel Multi-label Linear Discriminant Analysis (MLDA) method to take advantage of label correlations and explore the powerful classification capability of the classical LDA to deal with multi-label multi-class problems. Extensive experimental evaluations on five public multi-label data sets demonstrate excellent performance of our method.
منابع مشابه
Direct Multi-label Linear Discriminant Analysis
Multi-label problems arise in different domains such as digital media analysis and description, text categorization, multi-topic web page categorization, image and video annotation etc. Such a situation arises when the data are associated with multiple labels simultaneously. Similar to single label problems, multi label problems also suffer from high dimensionality as multi label data often hap...
متن کاملKernel Alignment Inspired Linear Discriminant Analysis
Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first define two kernels, data kernel and class indicator kernel. The problem is to find a subspace to maximize the alignment between subspace-transformed data kernel and class ind...
متن کاملMulti-label Classification: Inconsistency, Ambiguity and Class Balanced KNN Classification
Many existing researches employ one-vs-others approach to decompose a multi-label classification problem into a set of 2-class classification problems, one for each class. This approach is valid in traditional single-label classification. However, it incurs training inconsistency in multi-label classification, because a multi-label data point could belong to more than one class. In this work, w...
متن کاملMulti-Label Classification: Inconsistency and Class Balanced K-Nearest Neighbor
Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal wi...
متن کاملSide-Information based Linear Discriminant Analysis for Face Recognition
In recent years, face recognition in the unconstrained environment has attracted increasing attentions, and a few methods have been evaluated on the Labeled Faces in the Wild (LFW) database. In the unconstrained conditions, sometimes we cannot obtain the full class label information of all the subjects. Instead we can only get the weak label information, such as the side-information, i.e., the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010