Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm. Since NPE is a linear approximation to Locally Linear Embedding (LLE) algorithm, it has good neighborhood-preserving properties. Although NPE has been applied in many fields, it has limitations to solve recognition task. In this paper, a novel subspace method, named Kernel Fisher Neighborhood Preserving Embedding (KFNPE),...

Many real life applications brought by modern technologies often have multiple data sources, which are usually characterized by both attributes and pairwise similarities at the same time. For example in webpage ranking, a webpage is usually represented by a vector of term values, and meanwhile the internet linkages induce pairwise similarities among the webpages. Although both attributes and pa...

There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among the datasets severely degenerates the performance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels a...

Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results representation and single-label classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text abilities robust to natural distribution shifts. This success inspires us leverage multimodal for multi...

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-...

We describe in this report our audio scene recognition system submitted to the DCASE 2016 challenge [1]. Firstly, given the label set of the scenes, a label tree is automatically constructed. This category taxonomy is then used in the feature extraction step in which an audio scene instance is represented by a label tree embedding image. Different convolutional neural networks, which are tailor...

Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency. In this work we utilize a correspondence between rank constrained estimation and low dimensional label embeddings that uncovers a fast label embedding algorit...

Recently graph based dimensionality reduction has received a lot of interests in many fields of information processing. Central to it is a graph structure which models the geometrical and discriminant structure of the data manifold. When label information is available, it is usually incorporated into the graph structure by modifying the weights between data points. In this paper, we propose a n...