We first extend Hopfield networks to clustering bipartite graphs (words-to-document association) and show that the solution is the principal component analysis. We then generalize this via the min-max clustering principle into a self-aggregation networks which are composed of scaled PCA components via Hebb rule. Clustering amounts to an updating process where connections between different clust...

There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA. However, unlike tangent space PCA, our torus-PCA honors the cyclic topology of the data space whereas, unlike geodesic PCA, our torus-PCA produ...

We propose a subpattern-based principle component analysis (SpPCA). The traditional PCA operates directly on a whole pattern represented as a vector and acquires a set of projection vectors to extract global features from given training patterns. SpPCA operates instead directly on a set of partitioned subpatterns of the original pattern and acquires a set of projection sub-vectors for each part...

Time-series segmentation algorithms, such as methods based on Principal Component Analysis (PCA) and fuzzy clustering, are based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of process transitions. Hence, the key idea of this paper is to incorporate the first-principle model based state estimation into the segmentation algorithm t...

This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has many joints, for example our hand model has 15, and its high dimensionality makes it difficult to model hand motions. To make things easier, it is important to represent a hand motion in a low dimensional space. Principle component analysis (PCA) ha...

Two decades of research shows that Principle Component Analysis is effective and convenient for representation and recognition of human face images. It is a kind of subspace method. Many successful face recognition algorithms follow the subspace method and try to find better subspaces for face recognition. In this paper, we present the projection incorporated subspace method based on PCA. This ...

The principle behind to detect and track non-stationary object via a sequence of frames is addressed. The proposed strategy pushed the Normalized Cross-Correlation (NCCR) to track object by matching the template and updating the template is encouraged through Principal Component Analysis (PCA). This work remarked with exhaustive experiment and witnessed with comparative analysis over dataset re...

functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

The lip-reading recognition is reported with lip-motion features extracted from multiple video frames by three unsupervised learning algorithms, i.e., Principle Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Since the human perception of facial motion goes through two different pathways, i.e., the lateral fusifom gyrus for the invari...

Kajian tentang pengenalan wajah sampai saat ini masih banyak orang yang melakukan eksplorasi, hal dapat dilihat dari perkembangan teknologi Computer Vision diterapkan diberbagai aplikasi kehidupan. Tujuan penelitian adalah untuk mengidentifikasi seseorang berdasarkan ciri atau featur jenis kelamin pada kartu identitas mahasiswa di sebuah perguruan tinggi. Metode digunakan melalui pendekatan dat...