Prototypical classifier with distribution consistency regularization for generalized category discovery: A strong baseline.

Generalized Category Discovery (GCD) addresses a more realistic and challenging setting in semi-supervised visual recognition, where unlabeled data contains samples from both known and novel categories. Recently, prototypical classifier has shown prominent performance on this issue, with the Softmax-based Cross-Entropy loss (SCE) commonly employed to optimize the distance between a sample and prototypes. However, the inherent non-bijectiveness of SCE prevents it from resolving intraclass relations among samples, resulting in semantic ambiguity.

Interactive attack-defense for generalized person re-identification.

Generalized Person Re-Identification (GReID) aims to develop a model capable of robust generalization across unseen target domains, even with training on a limited set of observed domains. Recently, methods based on the Attack-Defense mechanism are emerging as a prevailing technology to this issue, which treats domain transformation as a type of attack and enhances the model's generalization performance on the target domain by equipping it with a defense module. However, a significant limitation of most existing approaches is their inability to effectively model complex domain transformations, largely due to the separation of attack and defense components.

Discriminative Projected Clustering via Unsupervised LDA.

This work focuses on the projected clustering problem. Specifically, an efficient and parameter-free clustering model, named discriminative projected clustering (DPC), is proposed for simultaneously low-dimensional and discriminative projection learning and clustering, from the perspective of least squares regression. The proposed DPC, a constrained regression model, aims at finding both a transformation matrix and a binary indicator matrix to minimize the sum-of-squares error.

Unsupervised Feature Selection With Weighted and Projected Adaptive Neighbors.

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels.

Robust Matrix Completion With Column Outliers.

Matrix completion, in essence, involves recovering a low-rank matrix from a subset of its entries. Most existing methods for matrix completion neglect two significant issues. First, in several practical applications, such as collaborative filtering, some samples may be corrupted completely.

Low Rank Regularization: A review.

Low Rank Regularization (LRR), in essence, involves introducing a low rank or approximately low rank assumption to target we aim to learn, which has achieved great success in many data analysis tasks. Over the last decade, much progress has been made in theories and applications. Nevertheless, the intersection between these two lines is rare.

Matrix Completion Based on Non-convex Low Rank Approximation.

Without any prior structure information, Nuclear Norm Minimization (NNM), a convex relaxation for Rank Minimization (RM), is a widespread tool for matrix completion and relevant low rank approximation problems. Nevertheless, the result derivated by NNM generally deviates the solution we desired, because NNM ignores the difference between different singular values. In this paper, we present a non-convex regularizer and utilize it to construct two matrix completion models.