8,159 research outputs found
Integrating simplified inverse representation and CRC for face recognition
© Springer International Publishing Switzerland 2015. The representation based classification method (RBCM) has attracted much attention in the last decade. RBCM exploits the linear combination of training samples to represent the test sample, which is then classified according to the minimum reconstruction residual. Recently, an interesting concept, Inverse Representation (IR), is proposed. It is the inverse process of conventional RBCMs. IR applies test samples’ information to represent each training sample, and then classifies the training sample as a useful supplement for the final classification. The relative algorithm CIRLRC, integrating IR and linear regression classification (LRC) by score fusing, shows superior classification performance. However, there are two main drawbacks in CIRLRC. First, the IR in CIRLRC is not pure, because the test vector contains some training sample information. The other is the computation inefficiency because CIRLRC should solve C linear equations for classifying the test sample respectively, where C is the number of the classes. Therefore, we present a novel method integrating simplified IR (SIR) and collaborative representation classification (CRC), named SIRCRC, for face recognition. In SIRCRC, only test sample information is fully used in SIR, and CRC is more efficient than LRC in terms of speed, thus, one linear equation system is needed. Extensive experimental results on face databases show that it is very competitive with both CIRLRC and the state-of-the-art RBCM
Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations
Thin film processing by means of sputter deposition inherently depends on the
interaction of energetic particles with a target surface and the subsequent
particle transport. The length and time scales of the underlying physical
phenomena span orders of magnitudes. A theoretical description which bridges
all time and length scales is not practically possible. Advantage can be taken
particularly from the well-separated time scales of the fundamental surface and
plasma processes. Initially, surface properties may be calculated from a
surface model and stored for a number of representative cases. Subsequently,
the surface data may be provided to gas-phase transport simulations via
appropriate model interfaces (e.g., analytic expressions or look-up tables) and
utilized to define insertion boundary conditions. During run-time evaluation,
however, the maintained surface data may prove to be not sufficient. In this
case, missing data may be obtained by interpolation (common), extrapolation
(inaccurate), or be supplied on-demand by the surface model (computationally
inefficient). In this work, a potential alternative is established based on
machine learning techniques using artificial neural networks. As a proof of
concept, a multilayer perceptron network is trained and verified with sputtered
particle distributions obtained from transport of ions in matter based
simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated
that the trained network is able to predict the sputtered particle
distributions for unknown, arbitrarily shaped incident ion energy
distributions. It is consequently argued that the trained network may be
readily used as a machine learning based model interface (e.g., by
quasi-continuously sampling the desired sputtered particle distributions from
the network), which is sufficiently accurate also in scenarios which have not
been previously trained
Adaptive Locality Preserving Regression
This paper proposes a novel discriminative regression method, called adaptive
locality preserving regression (ALPR) for classification. In particular, ALPR
aims to learn a more flexible and discriminative projection that not only
preserves the intrinsic structure of data, but also possesses the properties of
feature selection and interpretability. To this end, we introduce a target
learning technique to adaptively learn a more discriminative and flexible
target matrix rather than the pre-defined strict zero-one label matrix for
regression. Then a locality preserving constraint regularized by the adaptive
learned weights is further introduced to guide the projection learning, which
is beneficial to learn a more discriminative projection and avoid overfitting.
Moreover, we replace the conventional `Frobenius norm' with the special l21
norm to constrain the projection, which enables the method to adaptively select
the most important features from the original high-dimensional data for feature
extraction. In this way, the negative influence of the redundant features and
noises residing in the original data can be greatly eliminated. Besides, the
proposed method has good interpretability for features owing to the
row-sparsity property of the l21 norm. Extensive experiments conducted on the
synthetic database with manifold structure and many real-world databases prove
the effectiveness of the proposed method.Comment: The paper has been accepted by IEEE Transactions on Circuits and
Systems for Video Technology (TCSVT), and the code can be available at
https://drive.google.com/file/d/1iNzONkRByIaUhXwdEhOkkh_0d2AAXNE8/vie
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