RMFRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection

نویسندگان

چکیده

In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with adaptive structure learning (RMFRASL), which can select discriminative features from large amount of multimedia data to improve the performance classification and clustering tasks. RMFRASL integrates three models (robust factorization, learning, regularization) into unified framework. More specifically, factorization-based (RMFFS) model is proposed by introducing an indicator measure importance features, L21-norm adopted as metric enhance robustness selection. Furthermore, (RASL) based on self-representation capability samples designed discover geometric relationships original data. Lastly, regularization (SR) term learned graph structure, constrains selected preserve information in space. To solve objective function our RMFRASL, iterative optimization algorithm proposed. By comparing some state-of-the-art approaches several publicly available databases, advantage demonstrated.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Robust Asymmetric Bayesian Adaptive Matrix Factorization

Low rank matrix factorizations(LRMF) have attracted much attention due to its wide range of applications in computer vision, such as image impainting and video denoising. Most of the existing methods assume that the loss between an observed measurement matrix and its bilinear factorization follows symmetric distribution, like gaussian or gamma families. However, in real-world situations, this a...

متن کامل

Cold-start Active Learning with Robust Ordinal Matrix Factorization

We present a new matrix factorization model for rating data and a corresponding active learning strategy to address the cold-start problem. Coldstart is one of the most challenging tasks for recommender systems: what to recommend with new users or items for which one has little or no data. An approach is to use active learning to collect the most useful initial ratings. However, the performance...

متن کامل

Efficient Robust Matrix Factorization with Nonconvex Loss

Robust matrix factorization (RMF), which uses the `1-loss, often outperforms standard matrix factorization using the `2loss, particularly when outliers are present. The state-of-theart RMF solver is the RMF-MM algorithm, which, however, cannot utilize data sparsity. Moreover, sometimes even the (convex) `1-loss is not robust enough. In this paper, we propose the use of nonconvex loss to enhance...

متن کامل

Robust non-negative matrix factorization

Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) i...

متن کامل

Direct Robust Matrix Factorization for Anomaly Detection

Matrix factorization methods are extremely useful in many data mining tasks, yet their performances are often degraded by outliers. In this paper, we propose a novel robust matrix factorization algorithm that is insensitive to outliers. We directly formulate robust factorization as a matrix approximation problem with constraints on the rank of the matrix and the cardinality of the outlier set. ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Algorithms

سال: 2022

ISSN: ['1999-4893']

DOI: https://doi.org/10.3390/a16010014